{"as_of":"2026-08-07T15:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8d34146b2b7252bed34b52ffb00be093073072b50fa49ac658aa63f116a06cf3","coverage":[{"denominator":224,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T15:11:15.043247Z","state":"measured"},{"denominator":103,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":103,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T00:38:04.437923Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-13T07:42:30.430026Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"cited_work":{"arxiv_id":"2507.16541","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.16541","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A comprehensive data-centric overview of federated graph learning","venue":null,"work_id":"10169c66-6369-4940-b3aa-f38f861b329f","year":2025},"citing_paper":{"arxiv_id":"2605.11919","last_updated":"2026-05-12T10:35:43Z","snapshot_observed_at":"2026-07-06T23:23:39.723268Z","submitted_at":"2026-05-12T10:35:43Z","title":"STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-13T07:41:51.513934Z"},"links":{"cited_paper":"/paper/2507.16541","citing_paper":"/paper/2605.11919"},"observation_digest":"sha256:76e841db90528153dca3060894e19ede869a7827cb55c9c6611ebbec38647fb0","observation_id":"696eb033-a292-4681-adaf-7096f0e8b46c","resolution":{"observed_at":"2026-05-13T07:42:30.432959Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.16541","snapshot_observed_at":"2026-08-03T00:40:03.206406Z","title":"arXiv preprint arXiv:2507.16541 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.28708","last_updated":"2026-07-30T16:06:38Z","snapshot_observed_at":"2026-08-07T01:36:36.978863Z","submitted_at":"2026-07-30T16:06:38Z","title":"MMFGU: Multimodal Federated Graph Unlearning","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-03T00:40:03.206406Z"},"links":{"cited_paper":"/paper/2507.16541","citing_paper":"/paper/2607.28708"},"observation_digest":"sha256:bdf69f91080649319cdecca1f6368468577ac332f07f2fb0c18e08e75c1adf0c","observation_id":"10867100-8e12-475a-ac04-b6eea3daa460","resolution":{"observed_at":"2026-08-03T00:40:03.206406Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.16541","snapshot_observed_at":"2026-08-05T00:38:04.437923Z","title":"arXiv preprint arXiv:2507.16541 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00623","last_updated":"2026-08-01T12:29:38Z","snapshot_observed_at":"2026-08-06T23:17:32.648178Z","submitted_at":"2026-08-01T12:29:38Z","title":"Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity","version":1},"reference_index":103,"source":"arxiv_source","source_observed_at":"2026-08-05T00:38:04.437923Z"},"links":{"cited_paper":"/paper/2507.16541","citing_paper":"/paper/2608.00623"},"observation_digest":"sha256:3c03a702b2b796fcf34cedd282c1b12b2e43e33ae0efe15e0fdfc58ae6c5cf89","observation_id":"ea857860-3986-417e-9684-99fb62baff26","resolution":{"observed_at":"2026-08-05T00:38:04.437923Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2507.16541/citation-record","integrity":"/paper/2507.16541/integrity","json":"/paper/2507.16541/citation-record.json","paper":"/paper/2507.16541"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:11.862646Z","title":"Beyond i.i.d.: Non-iid thinking, informatics, and learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:11.862646Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:420f8e24a23da6e72628f2cdaef5fa08b56a48f82645ad851a864d190b876d2e","observation_id":"4502d5d1-7831-444a-9631-355081cb24eb","resolution":{"observed_at":"2026-08-06T15:11:11.862646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:11.922139Z","title":"Highly accurate protein structure prediction with alphafold,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:11.922139Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:6f816f887268ebc028b53ddce32cf12967c74159b7b1a63319846697eff3e98a","observation_id":"ad13d43c-9f75-4288-92f6-c6d18a7abb13","resolution":{"observed_at":"2026-08-06T15:11:11.922139Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:12.046110Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:12.046110Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:6507a61292a8dee3ef18c3ce69d3a76c7af59383e14eb4e775674b59f9778653","observation_id":"2cc1f7c2-ccef-4ee3-8967-c775215075e7","resolution":{"observed_at":"2026-08-06T15:11:12.046110Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:12.112382Z","title":"Graph Attention Networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:12.112382Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:44bfa8903379f6122dd577a72f43702202ece3dd4db14fb032182c77e6f3ad1f","observation_id":"f39ed59c-4232-4509-8354-07222d7eb492","resolution":{"observed_at":"2026-08-06T15:11:12.112382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:12.181274Z","title":"Data-centric graph learning: A survey,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:12.181274Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:8185abb0ddae5aa02c548abb271f53e56d69086ae793b2646060dbaea4557b9a","observation_id":"ca574c65-2cb5-4030-b00b-a762754cf87f","resolution":{"observed_at":"2026-08-06T15:11:12.181274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:12.269940Z","title":"Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:12.269940Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:b31f318d36389d356ea48cb4cc53013e49507b6898611c3e4868410fac05aaa4","observation_id":"09b6a2a4-8908-4571-9f4b-419e95b25776","resolution":{"observed_at":"2026-08-06T15:11:12.269940Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:12.365944Z","title":"A fast and high quality multilevel scheme for partitioning irregular graphs,","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:12.365944Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:0244b861554a4f0f6713ff3e612ede4c953dc22802fafb7e041e5e6e9ddbeca9","observation_id":"7b99b20f-6279-46be-beda-86cb57c51887","resolution":{"observed_at":"2026-08-06T15:11:12.365944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:12.451279Z","title":"Fast unfolding of communities in large networks,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:12.451279Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:84dec5c5f4af1fc1c5a3b42651155c9e8d7844bfae5b880db56503e750175034","observation_id":"f125f742-8da3-4464-b608-079150ba6d35","resolution":{"observed_at":"2026-08-06T15:11:12.451279Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.11750","last_updated":"2024-01-22T08:23:31Z","snapshot_observed_at":"2026-07-06T17:18:36.196373Z","submitted_at":"2024-01-22T08:23:31Z","title":"AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity","version":1},"cited_work":{"arxiv_id":"2401.11750","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.11750","snapshot_observed_at":"2026-08-06T15:11:18.390910Z","title":"AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity","venue":"cs.LG","work_id":"9b7b23e4-1206-42d0-bd3a-24b440571468","year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:12.516042Z"},"links":{"cited_paper":"/paper/2401.11750","citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:8a49c125647169a85b445807cb1f6bbc9781413256e73761289bcd1db8cf0d13","observation_id":"623eb9c2-09fe-4beb-9443-a58c327f595c","resolution":{"observed_at":"2026-08-06T15:11:18.396568Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.11755","last_updated":"2024-01-22T08:31:53Z","snapshot_observed_at":"2026-07-31T19:11:07.102844Z","submitted_at":"2024-01-22T08:31:53Z","title":"FedGTA: Topology-aware Averaging for Federated Graph Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.11755","snapshot_observed_at":"2026-08-06T15:11:12.565082Z","title":"Fedgta: Topology-aware averaging for federated graph learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:12.565082Z"},"links":{"cited_paper":"/paper/2401.11755","citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:18397c9325c14476d0161a6d5e6bc1e9273eb5d054c1ec5d0947d1baffe7fb1b","observation_id":"3e049b18-aefa-433a-b676-f04a11af8877","resolution":{"observed_at":"2026-08-06T15:11:12.565082Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.14061","last_updated":"2024-04-25T06:40:22Z","snapshot_observed_at":"2026-08-04T02:02:01.675856Z","submitted_at":"2024-04-22T10:19:02Z","title":"FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.14061","snapshot_observed_at":"2026-08-06T15:11:12.657935Z","title":"Fedtad: Topology- aware data-free knowledge distillation for subgraph federated learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:12.657935Z"},"links":{"cited_paper":"/paper/2404.14061","citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:c42b1b83bc617f55907bc05da50c750eef55d57d0dfe5cab6d00fc57f23d84b4","observation_id":"a81a293e-e02c-4ee1-9949-43f04fa517d8","resolution":{"observed_at":"2026-08-06T15:11:12.657935Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:12.737335Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:12.737335Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:4f63b5a1aa65dd2a9c011fbfa9cb8b0b43eca8e877c9248808bc5a7d97c53000","observation_id":"74efeaa5-201c-483a-bc40-b5b1ac25d82b","resolution":{"observed_at":"2026-08-06T15:11:12.737335Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:12.819426Z","title":"Communication-efficient learning of deep networks from decentralized data,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:12.819426Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:3b3d42660cafafaeab23fee2a7447df277ebab7186a8869cae8dd67eb52c532f","observation_id":"aee674c3-7978-4d72-af2c-eb4268f9e2cb","resolution":{"observed_at":"2026-08-06T15:11:12.819426Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:12.939843Z","title":"Inductive representation learning on large graphs,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:12.939843Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:dc7281ecb6278c5239b3dc71c73fbf749d06a710f751700ba8b4f99fba5cd0ce","observation_id":"81fc0273-d9c0-4d38-b27c-db5ee5c0769e","resolution":{"observed_at":"2026-08-06T15:11:12.939843Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:12.994431Z","title":"Graph neural networks: A review of methods and applications,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:12.994431Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:64149a5ca56f28e9927cecdad4ecddffce3149b8795cfb58fc56762694fb217f","observation_id":"53ea15dd-dca6-4f96-99c5-3d6e93532c16","resolution":{"observed_at":"2026-08-06T15:11:12.994431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:13.029705Z","title":"A comprehensive survey on graph neural networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:13.029705Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:b488f5539eb24afb4bbc5034305e38e21c4b33f0613f8b3c01ab8fbc0e0cfe1c","observation_id":"ce990083-9057-4e97-87cb-3b56a0462482","resolution":{"observed_at":"2026-08-06T15:11:13.029705Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.11099","last_updated":"2021-05-24T05:39:24Z","snapshot_observed_at":"2026-07-06T11:12:04.788175Z","submitted_at":"2021-05-24T05:39:24Z","title":"Federated Graph Learning -- A Position Paper","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.11099","snapshot_observed_at":"2026-08-06T15:11:13.142346Z","title":"Federated graph learning–a position paper,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:13.142346Z"},"links":{"cited_paper":"/paper/2105.11099","citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:78b7649639a21f9ba601b6ad541c69e93b4cbdb9f7e540da0edb33ae1c3e26bb","observation_id":"3af8de68-f74e-415c-8334-8d63a110170c","resolution":{"observed_at":"2026-08-06T15:11:13.142346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:13.230389Z","title":"Federated graph machine learning: A survey of concepts, techniques, and applications,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:13.230389Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:f78c3249004c38bb9215e0ad31a44dece5a37ee0212cd714021c5dd0a3fc0b5e","observation_id":"14eafe77-125d-4abf-9045-084cc9c053c3","resolution":{"observed_at":"2026-08-06T15:11:13.230389Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:13.311208Z","title":"Federated graph neural networks: Overview, techniques, and challenges,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:13.311208Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:d2630a023bd7be073e827618e4f73dda0360dbfbb30b24360c5fb6b5b9b69b2a","observation_id":"815dc383-1546-4420-b48f-8e89d5eda72d","resolution":{"observed_at":"2026-08-06T15:11:13.311208Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:13.465412Z","title":"Semi-decentralized federated ego graph learning for recommendation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:13.465412Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:2fa00ea7e511c4531db24631d494f8de8e106c656154973d25c5e6c468628dfd","observation_id":"68bb457d-a161-4fd3-a051-4ff5b2068e18","resolution":{"observed_at":"2026-08-06T15:11:13.465412Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:13.606196Z","title":"Link prediction based on graph neural networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:13.606196Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:0e417076f345262587b2b1ca1f71b08df8ed19936c01309215ad240e5dba390e","observation_id":"92fc2e32-dec9-4f4d-b677-1fd19ad59cd9","resolution":{"observed_at":"2026-08-06T15:11:13.606196Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:13.710854Z","title":"Graph convolutional networks with eigenpooling,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:13.710854Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:31e0ef89d29d63ea4b0f0e3d2e7efc010717becbc60dfb561bff87c4aaf84b7c","observation_id":"4cee9c75-6d0c-4951-b871-a478d3d679da","resolution":{"observed_at":"2026-08-06T15:11:13.710854Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:13.849387Z","title":"Federated node classification over graphs with latent link-type heterogeneity,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:13.849387Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:cd8b4697626bfade334950c373aee54f9e0771fa431e4fcb4440e1af1005c4e3","observation_id":"307876bf-7d24-42cf-8c4b-17553d72deab","resolution":{"observed_at":"2026-08-06T15:11:13.849387Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:13.979618Z","title":"Dynamic activation of clients and parameters for federated learning over het- erogeneous graphs,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:13.979618Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:c7d170c78648784b61427a0421872dedfad04a1639d9134886acac496391708b","observation_id":"a5a8b74f-f5ca-45b4-97a7-49aed6f41177","resolution":{"observed_at":"2026-08-06T15:11:13.979618Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.082018Z","title":"Federated heterogeneous graph neural network for privacy-preserving recommen- dation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.082018Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:629865c38bc5345e222c5a129a6c4b2b6d69e6672dc85d5b9e54038e69ce0267","observation_id":"96928ce6-377d-4479-9b74-f28960879cd4","resolution":{"observed_at":"2026-08-06T15:11:14.082018Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.223863Z","title":"A survey on knowledge graphs: Representation, acquisition, and applications,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.223863Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:88b5ea1832b3e6ae8cf39b5511a51af62903fda16026cb8a8d98120e32767fae","observation_id":"d13e1344-6b7a-4297-bd02-1ef238d071da","resolution":{"observed_at":"2026-08-06T15:11:14.223863Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.334739Z","title":"Knowledge graph embedding: A survey of approaches and applications,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.334739Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:7e2969ac65bc7c05e16b0e05120fd4094d75e60f09d4a58b28dcaaf775d6d74c","observation_id":"6d7f247f-19a1-47b1-9eb7-211cc3dc6591","resolution":{"observed_at":"2026-08-06T15:11:14.334739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.04692","last_updated":"2022-05-10T06:27:32Z","snapshot_observed_at":"2026-08-03T22:39:51.835402Z","submitted_at":"2022-05-10T06:27:32Z","title":"Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting","version":1},"cited_work":{"arxiv_id":"2205.04692","doi":null,"metadata_source":"pith","pith_arxiv_id":"2205.04692","snapshot_observed_at":"2026-08-06T15:11:18.218736Z","title":"Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting","venue":"cs.CL","work_id":"9e52cd08-6bb6-4614-96c1-8fe3ddae1455","year":2022},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.499591Z"},"links":{"cited_paper":"/paper/2205.04692","citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:df60e1e8a34a23ee0d76df463549cfc73b60433900438d7d6a697a148fdefb49","observation_id":"368ff5e4-d138-4818-97a9-f80b7d51b6f3","resolution":{"observed_at":"2026-08-06T15:11:18.223732Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.644036Z","title":"A federated graph to embedding approach for knowledge graph completion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.644036Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:95e49e93fb34d4fa567acca58f71d8339c4edd2ad996b681a9121e005532651e","observation_id":"4914ffa2-322f-4d0c-bbb2-77cb2c9cd047","resolution":{"observed_at":"2026-08-06T15:11:14.644036Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.700890Z","title":"Knowledge graph and knowledge reasoning: A systematic review,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.700890Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:1f0f366428d672f897d2d7d53c377ad6257808d19dcbb77a330eaaa0fdafbf03","observation_id":"fee5ad86-fafe-48df-a842-1bb208cefced","resolution":{"observed_at":"2026-08-06T15:11:14.700890Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.710925Z","title":"Ensemble-gnn: federated ensemble learning with graph neural networks for disease module discovery and classification,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.710925Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:21a937892c39845815685ddbcf47d37b48145f39f4ae41bce3baff12a9ddfa23","observation_id":"fb8883e4-4f32-4c96-9284-287b9d81fb22","resolution":{"observed_at":"2026-08-06T15:11:14.710925Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.724095Z","title":"Enhancing federated learning-based social recommendations with graph attention networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.724095Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:86fe96214400903ba1cd026226536a8ace8548071e5f7dc2ced6040ce04f9b19","observation_id":"d3ca3e15-2335-47fb-8556-14c4eb62c828","resolution":{"observed_at":"2026-08-06T15:11:14.724095Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.728979Z","title":"Federated learning enabled graph convolutional autoencoder and factorization machine for potential friendship prediction in social networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.728979Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:22313e7bb28b3166c0761959c6909318a7b7ecf45dd4d28e0197a2dcecf0e2dd","observation_id":"60ea76c2-920a-487f-a8f4-92b2e4e715a5","resolution":{"observed_at":"2026-08-06T15:11:14.728979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.733398Z","title":"Anti-money laundering in cryptocurrency via multi-relational graph neural network,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.733398Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:426a3721a6c9adddcb633016992957c7d5aa18e00eb7f966133a7407aa5adf60","observation_id":"fbe05941-51fc-44da-8071-7f1a6e63b23c","resolution":{"observed_at":"2026-08-06T15:11:14.733398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.738155Z","title":"Privacy-preserving graph convolution network for federated item recommendation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.738155Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:eb157463cfbbfe3567201c2df8a440f677e1b05df7ee98fbc4d9f7e83a28c677","observation_id":"1ffb10df-d598-415e-9c8e-4e4380793586","resolution":{"observed_at":"2026-08-06T15:11:14.738155Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.743400Z","title":"Fedego: privacy-preserving personalized federated graph learning with ego- graphs,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.743400Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:8f014e42492d7255a79aad7923dea3717650a9e6d2be39d83bd43f3ef38f00dc","observation_id":"57d78bdc-1611-47a9-9e25-e23a984de7b9","resolution":{"observed_at":"2026-08-06T15:11:14.743400Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.747457Z","title":"Federated graph classification over non-iid graphs,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.747457Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:8beffdba8071b3e263efd1f8baf0f49f3fc7e845eb811cf1b45a2c6c8e06986b","observation_id":"14985072-23f5-4d82-a155-1fa8119c6a4d","resolution":{"observed_at":"2026-08-06T15:11:14.747457Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.752116Z","title":"Federated learning on non-iid graphs via structural knowledge sharing,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.752116Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:3e6f7527c12d003773faae8eaae8b9e218a78a4efc5e7d614728b4af34bd4c85","observation_id":"9d697331-e5e4-49e3-a49d-55f6141ed88f","resolution":{"observed_at":"2026-08-06T15:11:14.752116Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.20105","last_updated":"2024-10-26T07:09:27Z","snapshot_observed_at":"2026-08-05T17:18:06.014831Z","submitted_at":"2024-10-26T07:09:27Z","title":"FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference","version":1},"cited_work":{"arxiv_id":"2410.20105","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.20105","snapshot_observed_at":"2026-08-06T15:11:18.196638Z","title":"FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference","venue":"cs.LG","work_id":"6b340a16-5a9f-4c38-9cd7-526ba9ab97f9","year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.756839Z"},"links":{"cited_paper":"/paper/2410.20105","citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:837152d82f4894be5837fc4e8a75a19c04ae271a3c941765c80220f9be8e4e08","observation_id":"ca87698b-50cb-4203-b8d0-f1c032f290b3","resolution":{"observed_at":"2026-08-06T15:11:18.201568Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.761289Z","title":"Mitigating the performance sacrifice in dp-satisfied federated settings through graph contrastive learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.761289Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:90988bd3f19b25c898f25037428961a9a9958c9c6f3339349109761671c5e3ce","observation_id":"0d697197-d57d-489e-a483-644299a8c0a7","resolution":{"observed_at":"2026-08-06T15:11:14.761289Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19229","last_updated":"2025-02-21T01:19:55Z","snapshot_observed_at":"2026-07-31T02:54:23.596865Z","submitted_at":"2024-12-26T14:16:15Z","title":"Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning","version":2},"cited_work":{"arxiv_id":"2412.19229","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.19229","snapshot_observed_at":"2026-08-06T15:11:18.175342Z","title":"Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning","venue":"cs.LG","work_id":"c4b7532a-9012-4ab0-a2e6-c09f59e7ad1b","year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.765919Z"},"links":{"cited_paper":"/paper/2412.19229","citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:218fd0c5ac0a4ff893611bdeb3ae934c104e85de3ad4e3318f5e6273b6cd3213","observation_id":"ea81255b-ba9b-4a5a-a46e-3167e1710b78","resolution":{"observed_at":"2026-08-06T15:11:18.180433Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.771110Z","title":"Distributed backdoor attacks on federated graph learning and certified defenses,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.771110Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:e6c2bb1eddc48a44dfa6051205651f70f23d742930892889bc01094101e859ef","observation_id":"272d7b03-9222-4d90-9071-a0450ed11e3f","resolution":{"observed_at":"2026-08-06T15:11:14.771110Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.775722Z","title":"Ni-gdba: Non-intrusive distributed backdoor attack based on adaptive perturbation on federated graph learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.775722Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:aae8278367c5fef39a94fc34dc1aaa7c06bd5b9fe99a2eaff4dac983bbce72d8","observation_id":"d1279afb-a078-412f-9b31-722c230308f8","resolution":{"observed_at":"2026-08-06T15:11:14.775722Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.17533","last_updated":"2024-10-23T03:25:55Z","snapshot_observed_at":"2026-07-06T19:38:13.816410Z","submitted_at":"2024-10-23T03:25:55Z","title":"FedGMark: Certifiably Robust Watermarking for Federated Graph Learning","version":1},"cited_work":{"arxiv_id":"2410.17533","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.17533","snapshot_observed_at":"2026-08-06T15:11:18.152651Z","title":"FedGMark: Certifiably Robust Watermarking for Federated Graph Learning","venue":"cs.CR","work_id":"d236ccc7-6510-4194-9cf6-d85247ba5e65","year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.781109Z"},"links":{"cited_paper":"/paper/2410.17533","citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:742b041ec018ff0f8b3468767591a95e1d8057e86d84843a71e9b3ac0ccdc673","observation_id":"48480d1c-5ba7-4665-9053-46265df271a2","resolution":{"observed_at":"2026-08-06T15:11:18.157596Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.785871Z","title":"Fedgraph: Federated graph learning with intelligent sampling,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.785871Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:716683f49c73021b320b46e1650e968330e1aa793efb98185e02f2361ce7ab82","observation_id":"ebd1c30f-f172-4865-a2ef-b8562650ede3","resolution":{"observed_at":"2026-08-06T15:11:14.785871Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.790166Z","title":"Fedgcn: Convergence- communication tradeoffs in federated training of graph convolutional 18 networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.790166Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:dde2c763cbe0393310e0d3ed548ed8fce1d54d57fa264eb0a51e09ea50f74e82","observation_id":"e0ab70ad-1dd7-409b-a794-9757d6dc5fba","resolution":{"observed_at":"2026-08-06T15:11:14.790166Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.795261Z","title":"Graphfl: A federated learning framework for semi-supervised node classification on graphs,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.795261Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:1405648c3cf00eb44be2f8c0feb0dd791ada1ddff12950e49f7de9fa828cd26d","observation_id":"b6fbc3ce-69d0-4f9b-b038-74f8c2543251","resolution":{"observed_at":"2026-08-06T15:11:14.795261Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.03170","last_updated":"2021-05-07T11:27:23Z","snapshot_observed_at":"2026-07-06T11:07:11.782598Z","submitted_at":"2021-05-07T11:27:23Z","title":"FedGL: Federated Graph Learning Framework with Global Self-Supervision","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.03170","snapshot_observed_at":"2026-08-06T15:11:14.799756Z","title":"Fedgl: federated graph learning framework with global self-supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.799756Z"},"links":{"cited_paper":"/paper/2105.03170","citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:b280dc4df49b3fa747fd3f7c5da8e77cc14daf928afb38f66a1412dce9f38ab0","observation_id":"682a4f46-6264-4653-896a-fd4019753584","resolution":{"observed_at":"2026-08-06T15:11:14.799756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.804439Z","title":"Personalized subgraph federated learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.804439Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:eaba5736acb92093d14f36afac7f07a62ae8893a6c27b26d5da8abea084421e6","observation_id":"fffd113f-ae54-40af-890d-83db0cd08ca9","resolution":{"observed_at":"2026-08-06T15:11:14.804439Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.809144Z","title":"Federated graph learning under domain shift with generalizable prototypes,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.809144Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:057f34271c3e2422fdf12b238eb7666499c631025fee38ca060e6ecd12780047","observation_id":"5f19c7e8-10d0-4a68-8f8a-45a7f352c1c2","resolution":{"observed_at":"2026-08-06T15:11:14.809144Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.813461Z","title":"Federated graph semantic and structural learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.813461Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:53e6d0857cff8fd588f999066ee275aadda700e36891b8562aa31dccfcdfba7a","observation_id":"469c7df4-fa5a-4b94-bf96-37d11e3a86e7","resolution":{"observed_at":"2026-08-06T15:11:14.813461Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.818416Z","title":"Subgraph federated learning with missing neighbor generation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.818416Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:9427621d3aa75561e68ec0a454af1adbdc42ffe6fdc1e2d091d99a9c7f718a9b","observation_id":"eee52258-8a70-4d4f-9018-7f8ea8a7ac06","resolution":{"observed_at":"2026-08-06T15:11:14.818416Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.822910Z","title":"Deep efficient private neighbor generation for subgraph federated learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.822910Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:aa25bec552119d8754809602fdf3c3f7b7b5e25c4ac1a0d8696c189f93447e15","observation_id":"65480dc0-c702-454a-a04d-58f67cde7801","resolution":{"observed_at":"2026-08-06T15:11:14.822910Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.827011Z","title":"Model-heterogeneous federated graph learning with prototype propagation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.827011Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:76d1f1ec263dc298e9ea26b31aba6d81d770b417488155cca313766084e068e3","observation_id":"d43ea898-72ba-4689-9cd5-de2dde664ca6","resolution":{"observed_at":"2026-08-06T15:11:14.827011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.831208Z","title":"Fedsg: A personalized subgraph federated learning framework on multiple non-iid graphs,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.831208Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:62f36d584768f06b4687b07caab1cb18042c96397e037f896af2aeee649021ac","observation_id":"6960a970-8191-453f-8c7d-c8f35db54893","resolution":{"observed_at":"2026-08-06T15:11:14.831208Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.835458Z","title":"Federated graph learning with structure proxy alignment,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.835458Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:2f0d0fc02ace50e1e251c2949250ed4afe3370307c63da9a5746b0256d731121","observation_id":"dd191eb3-a72f-47bd-9d92-741f9c44f17c","resolution":{"observed_at":"2026-08-06T15:11:14.835458Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.840381Z","title":"Federated learning over coupled graphs,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.840381Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:59c70eff73f104a2bb786a52536229180d1cf1775b823a64841d61d4d463453b","observation_id":"ac7bde5b-e5ba-4037-9c82-49d1fe79945e","resolution":{"observed_at":"2026-08-06T15:11:14.840381Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.845152Z","title":"Hifgl: A hierarchical framework for cross-silo cross-device federated graph learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.845152Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:4af04082b548fd4060a74b8e2a942830cbcbff4275677181bad4a4290b0074d8","observation_id":"e132c7c6-2c15-4841-80ee-f839e0c18ae8","resolution":{"observed_at":"2026-08-06T15:11:14.845152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.849872Z","title":"Modeling inter-intra heterogeneity for graph federated learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.849872Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:b941cf1692fa37b98f556e4240bb4a3f1f7c9f29a0847b8716cd4577b4a0f1bb","observation_id":"f7321dd4-fcc2-408b-9fb0-97c493aa68af","resolution":{"observed_at":"2026-08-06T15:11:14.849872Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.854915Z","title":"Federated graph condensation with information bottleneck principles,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.854915Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:4389782991be4f3316d392902c779fb5a558cb931a81d871a2ac436d0b30535f","observation_id":"3ffd5881-4136-4ce7-bb2f-467f8021c9e2","resolution":{"observed_at":"2026-08-06T15:11:14.854915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.05535","last_updated":"2021-07-06T19:00:21Z","snapshot_observed_at":"2026-07-06T09:27:31.979054Z","submitted_at":"2020-06-09T22:36:06Z","title":"Locally Private Graph Neural Networks","version":9},"cited_work":{"arxiv_id":"2006.05535","doi":null,"metadata_source":"pith","pith_arxiv_id":"2006.05535","snapshot_observed_at":"2026-08-06T15:11:18.112546Z","title":"Locally Private Graph Neural Networks","venue":"cs.LG","work_id":"21a86584-b287-4126-ad4b-0734164a69be","year":2020},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.859531Z"},"links":{"cited_paper":"/paper/2006.05535","citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:fe28dc616911ed5bca334cb27806f99968ace3be4f758df46ad10554d61b7433","observation_id":"40434be8-2e24-4e36-a728-5d5096f90b84","resolution":{"observed_at":"2026-08-06T15:11:18.117753Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.863936Z","title":"Fedwalk: Communication efficient federated unsupervised node embedding with differential privacy,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.863936Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:f821552d06a068d28e29c7ef2e929a6a170100a436a362ac7dc3e0cb7daf6ab9","observation_id":"279c73ad-ac6e-4460-8411-ce3c24e05f81","resolution":{"observed_at":"2026-08-06T15:11:14.863936Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.868037Z","title":"Lumos: Heterogeneity-aware federated graph learning over decentralized devices,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.868037Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:128b39be8f0c5bd9b6a531a94134a029ba0ac6259fd7adb8812c2daa0fdf92ca","observation_id":"5386d880-4c18-4338-b91e-4b5f5b95c4c3","resolution":{"observed_at":"2026-08-06T15:11:14.868037Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.873091Z","title":"Federated node classification over distributed ego-networks with secure contrastive embedding sharing,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.873091Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:de28c16a8934b4ae8f78e38c0866d7a84bc8f7160f96aa3593139407f5af246b","observation_id":"0b59fa09-e6bc-443c-882c-0a95846b1859","resolution":{"observed_at":"2026-08-06T15:11:14.873091Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.09531","last_updated":"2023-03-16T17:47:55Z","snapshot_observed_at":"2026-08-03T12:01:42.412020Z","submitted_at":"2023-03-16T17:47:55Z","title":"GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data","version":1},"cited_work":{"arxiv_id":"2303.09531","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.09531","snapshot_observed_at":"2026-08-06T15:11:18.021053Z","title":"GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data","venue":"cs.LG","work_id":"1308d547-a87c-4283-8253-e35496bf1556","year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.882245Z"},"links":{"cited_paper":"/paper/2303.09531","citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:d02df9634a6ab84b8e48ad18c55e5a5275a0833da4029fb08cb31d2b037a7c88","observation_id":"846cd099-3b91-47df-b0e9-81cbed90c763","resolution":{"observed_at":"2026-08-06T15:11:18.025971Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.887318Z","title":"Svfgnn: A privacy-preserving vertical federated graph neural network model training framework based on split learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.887318Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:229d0280617e6d629319cd5cad25a6ab641ee2b386620b70e802518a759b51a8","observation_id":"e2200efc-4ea5-4d0f-b2e9-2d0f10e5cb60","resolution":{"observed_at":"2026-08-06T15:11:14.887318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.11903","last_updated":"2022-04-25T03:04:30Z","snapshot_observed_at":"2026-07-06T09:22:58.231918Z","submitted_at":"2020-05-25T03:12:18Z","title":"Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.11903","snapshot_observed_at":"2026-08-06T15:11:14.892908Z","title":"Vertically federated graph neural network for privacy-preserving node classification,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.892908Z"},"links":{"cited_paper":"/paper/2005.11903","citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:0d78c29a0811919f860cf03826e6dfa2d0ebd7224d2481d0fa3213b7da7fa61e","observation_id":"10c6f55a-6819-47a4-98b5-c46a62b8a25d","resolution":{"observed_at":"2026-08-06T15:11:14.892908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.898284Z","title":"Embedding representation of academic heterogeneous information networks based on federated learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.898284Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:c58b1ecd7e908c66b3e2ea6c4227b98bb63cbdab9601f5ff126f1a9edb7767fd","observation_id":"769bb0eb-a0fa-4e21-b2a7-d51a7988e5f1","resolution":{"observed_at":"2026-08-06T15:11:14.898284Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.902548Z","title":"Federated heterogeneous graph neural network for privacy-preserving recommen- dation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.902548Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:2eb3ff85eafc95634048bf64b1d686f834b347d05980cb7736daebf1c7410d22","observation_id":"6729b59b-3caa-4cc3-8888-970ab99e7168","resolution":{"observed_at":"2026-08-06T15:11:14.902548Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.906977Z","title":"Fedhgn: A federated framework for heterogeneous graph neural networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.906977Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:9f4b024c9f61ea95247dad552b74a32f84ddcaa01bcc31ab9800d1e19328d0b5","observation_id":"c54630c1-99f6-4664-bb43-0a760924b9e6","resolution":{"observed_at":"2026-08-06T15:11:14.906977Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.911510Z","title":"Self-simulation and meta-model aggregation based heterogeneous graph coupled federated learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.911510Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:70e6c580ae3fc1eb861d1f3c39225607a513b80e3b8e7d818482dc63e397540a","observation_id":"e9c59c6f-7aba-4608-ab7e-a8d4b65c0e2b","resolution":{"observed_at":"2026-08-06T15:11:14.911510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.916151Z","title":"Horizontal federated heterogeneous graph learning: A multi-scale adaptive solution to data distribution challenges,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.916151Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:36e347311a913dfffef7f2d2c4c2d7dce7cc43d2ba9e4e9c9d15dbcc64eb9c19","observation_id":"26ce0a03-e22e-478b-9d77-f33b04d01b9a","resolution":{"observed_at":"2026-08-06T15:11:14.916151Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.920449Z","title":"Fedhgn: a federated framework for heterogeneous graph neural networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.920449Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:126be821f2e87fcdc862095dd90968be992b97743cc78300afe20a84064e3269","observation_id":"86da7316-c8a3-43e1-955c-7b4a0d0839f6","resolution":{"observed_at":"2026-08-06T15:11:14.920449Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04925","last_updated":"2021-03-01T08:27:46Z","snapshot_observed_at":"2026-08-02T03:09:07.205458Z","submitted_at":"2021-02-09T16:30:53Z","title":"FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04925","snapshot_observed_at":"2026-08-06T15:11:14.925144Z","title":"Fedgnn: Federated graph neural network for privacy-preserving recommendation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.925144Z"},"links":{"cited_paper":"/paper/2102.04925","citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:ab784544f5017d6158ab7f70b1e3ae340b3a1e5744ff76fa07b04e9a95096d62","observation_id":"be41806b-6ecc-41a7-87d6-ed9199e187c2","resolution":{"observed_at":"2026-08-06T15:11:14.925144Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.930270Z","title":"Splitgnn: Spectral graph neural network for fraud detection against heterophily,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.930270Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:b5c77f5c7c9695887264909e33221a0ff3a25f1fdc2f5a9420f6fa8b39b27381","observation_id":"022cb47f-afd4-4652-bf3c-84437f2b4d66","resolution":{"observed_at":"2026-08-06T15:11:14.930270Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.935194Z","title":"Vertical federated graph neural network for rec- ommender system,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.935194Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:24620d2126fb2dda1b456312350923738243fcbc664c0dd12dbc0f5f6691129b","observation_id":"95e4cc9d-bdfb-4ffa-8088-c36687b45e51","resolution":{"observed_at":"2026-08-06T15:11:14.935194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.940458Z","title":"Meta-learning based knowledge extrapolation for knowledge graphs in the federated setting,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.940458Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:436b9e358af8698e5af7f4084ee1e39780f4d4680576a9e6a4dae3af596426dd","observation_id":"1feebccc-7d05-41c6-87dd-c3164637d307","resolution":{"observed_at":"2026-08-06T15:11:14.940458Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.944994Z","title":"Federated knowledge graph completion via embedding-contrastive learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.944994Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:5de5f5ce8b52a70298049310a4be23e7448fc902991650e1161fe77582e435cf","observation_id":"3b60ebeb-3f5c-420c-819b-cbc7fb101882","resolution":{"observed_at":"2026-08-06T15:11:14.944994Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"6842.35823","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:17.885302Z","title":"Fedrule: Federated rule recommendation system with graph neural networks,","venue":null,"work_id":"8a49ac3f-7563-458d-a50b-4ea50733659c","year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.949703Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:1b55e7d63f19384292d2f15beaf7d8b7adcf3ded72d066d1b54adc9a6816822c","observation_id":"87cf2c08-0694-4b5a-8c92-ac0a870f12dd","resolution":{"observed_at":"2026-08-06T15:11:17.893312Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.954024Z","title":"Fedcomp: A federated learning compression framework for resource-constrained edge computing devices,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.954024Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:94a61cc8b628df3552c0c466064f5fde3a0fb2179450e975efd2ec5b512e68d3","observation_id":"11b7783b-076b-4e4c-9852-288e18a1427b","resolution":{"observed_at":"2026-08-06T15:11:14.954024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.958732Z","title":"Fedge: Break the scalability limitation of graph neural network with federated graph embedding,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.958732Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:6e749bb0ffc83f9651dff7d10c8c4e2abf114c807dafb1fbf2726ab1c69ac440","observation_id":"e3eadf14-52e9-4225-bda0-a02ca618be17","resolution":{"observed_at":"2026-08-06T15:11:14.958732Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.963281Z","title":"Gfedkg: Gnn-based federated embedding model for knowledge graph completion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.963281Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:c488bb56e9b638981bb22fb49136439f7337f609075bec7ff653e4a23a8f810d","observation_id":"c6e4024a-21b7-449e-8e86-bd7e121e3409","resolution":{"observed_at":"2026-08-06T15:11:14.963281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.967958Z","title":"Federated learning-based cross-enterprise recommendation with graph neural networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.967958Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:32eac3a08c720fc5d7e0ebb0d0756ee9b4508586579741e551e8be9990887a25","observation_id":"98cec825-054c-421f-8ad5-14d6574af3e5","resolution":{"observed_at":"2026-08-06T15:11:14.967958Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.972622Z","title":"Fedrkg: A privacy-preserving federated recommendation framework via knowledge graph enhance- ment,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.972622Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:d22013ca296761b2361ff5e9f0201d0bbfca2464267048591a671b722586d85e","observation_id":"b9dcfe6f-c373-45d2-8c8f-6e2f425cbea7","resolution":{"observed_at":"2026-08-06T15:11:14.972622Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.977010Z","title":"Fedkgrec: privacy- preserving federated knowledge graph aware recommender system,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.977010Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:4bf05dabf471cce66a2a59080da5e81df840083bdc0473a1366a90efc5953a86","observation_id":"34157885-7897-406a-a23e-789580719c40","resolution":{"observed_at":"2026-08-06T15:11:14.977010Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.981829Z","title":"Fgc: Gcn-based federated learning approach for trust industrial service recommendation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.981829Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:b980ea0e5adadfcfc663ea9e57d778704aa58f74af344252caac1cd61d8a99ea","observation_id":"b599407b-53ae-436a-9901-869c8ee351f1","resolution":{"observed_at":"2026-08-06T15:11:14.981829Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.986419Z","title":"Fdrs: Federated diversified recommender system based on heterogeneous graph convolutional network,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.986419Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:946d3ac2b0b8aa24dd5862f2c2dbb20833652bf56b07c09aaea5372076fe7782","observation_id":"fc08c435-e068-4fe9-859b-8f2e92ee7c1f","resolution":{"observed_at":"2026-08-06T15:11:14.986419Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.991158Z","title":"Fedfast: Going beyond average for faster training of federated recommender systems,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.991158Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:ade86f2689516879f02c66e1baeb6b57eaad64c603e2dba0c3193638de688656","observation_id":"ed233a8f-62a4-4069-8cee-ce15e969d564","resolution":{"observed_at":"2026-08-06T15:11:14.991158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:14.995913Z","title":"Fedrec: Federated recommen- dation with explicit feedback,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:14.995913Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:c21efd07ca01c744b71941889212ae200efc07c1e6ec509f5ac12f360b094486","observation_id":"fed871c2-5420-4d51-a5a0-7acf38386a46","resolution":{"observed_at":"2026-08-06T15:11:14.995913Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:15.000123Z","title":"Communication-efficient federated recommendation model based on many-objective evolutionary algorithm,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:15.000123Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:c43ba643da3a2c9804a8551dc9e8c7d001f12bac7977d140487cba393288dad0","observation_id":"e7b9d3e5-ce15-4613-9de2-4345306fe7fa","resolution":{"observed_at":"2026-08-06T15:11:15.000123Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:15.004443Z","title":"Edge- enabled federated sequential recommendation with knowledge-aware transformer,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:15.004443Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:49ba9452face759643820e77391541c0679a7c99ac1959cf90a3da938e9c7cdf","observation_id":"c3526078-f3d1-46ef-a82e-5bab292ea6b5","resolution":{"observed_at":"2026-08-06T15:11:15.004443Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:15.008673Z","title":"Fedpoirec: Privacy-preserving federated poi recommendation with social influence,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:15.008673Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:63e882cfc5f98350f4974ae27e54a86554a03904ebdcc3706bb6be34b160ba42","observation_id":"45ca9a70-d51f-4f4e-b456-52c3533eec77","resolution":{"observed_at":"2026-08-06T15:11:15.008673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:15.013117Z","title":"Federated neural collaborative filtering,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:15.013117Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:8beb8154c0ff76772cefc3d15de878cdb6dc38ca6cf31fb1ef277799d4ce6577","observation_id":"6a7e2a19-b8b6-4a94-9a78-ecf9397681af","resolution":{"observed_at":"2026-08-06T15:11:15.013117Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:15.017126Z","title":"Federated learning based driver recommendation for next generation transportation system,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:15.017126Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:bd7730d1038f5f9242c324d28e97b45714e8b0d527ef4b8171dcbfa660de6b1f","observation_id":"7fa8c7fc-5a85-4ba8-8de9-67adf9d40fa5","resolution":{"observed_at":"2026-08-06T15:11:15.017126Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:15.021211Z","title":"Personalized federated recommendation via joint representation learning, user clustering, and model adaptation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:15.021211Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:a915f02521fbc3d2126f13660a65a3b30983f4a6273e43f7d64604a31d8e8a58","observation_id":"6007cb41-0117-4111-bac0-0db48a23186a","resolution":{"observed_at":"2026-08-06T15:11:15.021211Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:15.025624Z","title":"Gpfedrec: Graph-guided personalization for federated recommendation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:15.025624Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:0eb1f130be6d5ff04d0069979717e84d970815697728559d1fb03a2698f0a501","observation_id":"7aae75f0-08a6-4758-9335-1d25a37cd596","resolution":{"observed_at":"2026-08-06T15:11:15.025624Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:15.030118Z","title":"Perfedrec++: Enhancing personalized federated recommendation with self-supervised pre-training,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:15.030118Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:8d2075ca56b16824a2e159527cc1a19f8be9e8c72857195d821e51d942a7cf7f","observation_id":"e07cd2b1-7546-4269-b391-ebd8ed581bb3","resolution":{"observed_at":"2026-08-06T15:11:15.030118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:15.034359Z","title":"Hetefedrec: Federated recommender systems with model heterogeneity,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":102,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:15.034359Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:285be034689c5ac2f5d6f78be4f65556d90ec6597216e7a3e92224175bae5c38","observation_id":"ff9f506e-2d64-4a5b-b3a5-517debfe4ebd","resolution":{"observed_at":"2026-08-06T15:11:15.034359Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:11:15.038834Z","title":"Towards personalized privacy: User-governed data contribution for federated recommendation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":103,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:15.038834Z"},"links":{"citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:00eebe3dfc249069539b8c8c90883bc8036f7d99ec03a602bfee95cd431f8e9e","observation_id":"f593e04b-1457-4d3b-b9c5-0aafef787005","resolution":{"observed_at":"2026-08-06T15:11:15.038834Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.08249","last_updated":"2024-11-04T02:50:41Z","snapshot_observed_at":"2026-07-06T19:31:23.072307Z","submitted_at":"2024-10-10T12:19:51Z","title":"Federated Graph Learning for Cross-Domain Recommendation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.08249","snapshot_observed_at":"2026-08-06T15:11:15.043247Z","title":"Federated graph learning for cross-domain recommendation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":104,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:15.043247Z"},"links":{"cited_paper":"/paper/2410.08249","citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:c60a26ad8794ef4afdb8eba61e05029f931c8d0e3ba29ec10696946f3b0172f1","observation_id":"946c0442-8749-4089-a3ca-00fb9a536efb","resolution":{"observed_at":"2026-08-06T15:11:15.043247Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T15:05:08.799914Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":92,"verified_exact":7,"verified_fuzzy":0},"total_outbound_references":224},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 100 of 224 outbound references and 3 inbound Pith citation observations for arXiv:2507.16541."}