{"as_of":"2026-08-11T19:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:75795b0e7aa33e32db776860991608465390580163006acb7bcba3b73b53d101","coverage":[{"denominator":89,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":89,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T10:41:12.565965Z","state":"measured"},{"denominator":89,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":89,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.16441/citation-record","integrity":"/paper/2412.16441/integrity","json":"/paper/2412.16441/citation-record.json","paper":"/paper/2412.16441"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.102691Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.102691Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:1cdd8a59f3b18055643ab181cc78d132961482e639291b70783adeb5f6204efc","observation_id":"84545f00-1d7f-48bb-a9a4-b9b024a8bc82","resolution":{"observed_at":"2026-08-11T10:41:12.102691Z","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":"2026-08-11T10:41:12.109705Z","title":"L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.109705Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:d597dde1a2e65ff709473acb48e1ab9825c1dff10deded28feaa1f2baca1dea2","observation_id":"53af88fc-f0b0-48e7-9e25-5babe28b966d","resolution":{"observed_at":"2026-08-11T10:41:12.109705Z","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":"2026-08-11T10:41:12.115914Z","title":"When to pre-train graph neural networks? from data generation perspective! In KDD, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.115914Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:bbf90e547786b2015d9838debcff136298fb99275b1a5be999e4b6c4398beb02","observation_id":"b9f2fa38-6730-4ed3-bf82-652a7c5a1161","resolution":{"observed_at":"2026-08-11T10:41:12.115914Z","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":"2026-08-11T10:41:12.122055Z","title":"K., Shah, N., and Wang, Z","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.122055Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:cd328c14dd50cf5c8d332eaac7bac15d3196a4522b6d5de3d2dcb1921c86c826","observation_id":"eed3fd09-c34f-484d-9859-6b2ce8e1187d","resolution":{"observed_at":"2026-08-11T10:41:12.122055Z","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":"2026-08-11T10:41:12.128527Z","title":"Can graph neural networks count substructures? In NeurIPS, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.128527Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:f3049d89a33bf8eedaf62597b70d90f5ccb5a8142ee6d05d5c9db4532271b6ad","observation_id":"02e546ff-ff78-44a1-8bf7-3c6a5485b41c","resolution":{"observed_at":"2026-08-11T10:41:12.128527Z","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":"2026-08-11T10:41:12.133258Z","title":"Text-space graph foundation models: Comprehensive benchmarks and new insights","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.133258Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:5348f2babaacbf2d851fcdf337f9416d903c8359b90c952cb5a1def74d744a52","observation_id":"ff8925df-fd00-4170-bebd-e9d5d11b7cab","resolution":{"observed_at":"2026-08-11T10:41:12.133258Z","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":"2026-08-11T10:41:12.138219Z","title":"Fastgas: Fast graph-based annotation selection for in-context learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.138219Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:9061f8507c96b34385c536cffb1bfe875c1f0031ee48042ea2cb13dc146e20b1","observation_id":"c89422b7-4b09-45ba-9f3f-fd85c732e057","resolution":{"observed_at":"2026-08-11T10:41:12.138219Z","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":"2026-08-11T10:41:12.143422Z","title":"and Jegelka, S","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.143422Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:feb81c6a122e485ed72d49cdd26e49dc0e82d41622bc461d9543fce4e47fd227","observation_id":"19072354-817f-4116-b8be-e30dae6e6b73","resolution":{"observed_at":"2026-08-11T10:41:12.143422Z","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":"2026-08-11T10:41:12.148693Z","title":"On the generalization ability of unsupervised pretraining","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.148693Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:4bbba5208cd011885e88b3aa5901ae21911e1f846e10e88cb23b5def819bc8e4","observation_id":"44e36963-0f79-4632-b0c5-dbdf290be861","resolution":{"observed_at":"2026-08-11T10:41:12.148693Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:14.260226Z","title":"Graph prototypical networks for few-shot learning on attributed networks","venue":null,"work_id":"5b6835d1-2d4d-4933-a49c-26f16f4d7935","year":2020},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.154040Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:e2f132bd3715a1bd533edbc7b3bcdb1c8dd92c8942296d47fb5fb537e4d3e5ff","observation_id":"b1ccc9b8-889d-4eb3-84d2-aedcbf7ca661","resolution":{"observed_at":"2026-08-11T10:41:14.266498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:14.242239Z","title":"Learning theory can (sometimes) explain generalisation in graph neural networks","venue":null,"work_id":"350c1c97-dbdf-49de-bd4e-60d10a1850be","year":2021},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.159295Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:efad80e828a2e0c8b0659aa0b80bd96caef68b7f6bced804d55dc1fe547ad193","observation_id":"41b8e225-0adb-496e-91a7-26b73e495039","resolution":{"observed_at":"2026-08-11T10:41:14.247787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:14.222248Z","title":"Heterogeneous temporal graph neural network","venue":null,"work_id":"121dda3f-5156-49d1-a4f5-e5c27f1e779d","year":2022},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.164500Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:6f6610b8b74233c00c873e8acb125f49517d5943f78f8948d3f10164aeb85d28","observation_id":"dba1de7b-2406-4700-b012-a8a2a3c4a317","resolution":{"observed_at":"2026-08-11T10:41:14.229089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:14.199205Z","title":"Taglas: An atlas of text-attributed graph datasets in the era of large graph and language models","venue":null,"work_id":"dc847611-dd9a-400c-84bc-01018d3d8b70","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.169379Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:ca3d3ef408a61a82f0e68ec290b10fde0a0fe348ecb63be9906c06198fecf88b","observation_id":"d07f3ac5-2ca3-44a0-9b74-30b1a852d8ac","resolution":{"observed_at":"2026-08-11T10:41:14.205244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:14.180336Z","title":"The development of social network analysis","venue":null,"work_id":"09dc5628-95cf-46a2-8666-57c3cffecff6","year":2004},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.174452Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:2d08ef6fe749215e47b635f9bae8b021c873adb763574cdcc67fdc938fa2210d","observation_id":"d7e12fa6-e9f3-4d51-ae33-4a8a140254ab","resolution":{"observed_at":"2026-08-11T10:41:14.185662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:14.162828Z","title":"Towards foundation models for knowledge graph reasoning","venue":null,"work_id":"91b598b6-b575-4da0-a681-4bb0b95ea24c","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.179430Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:31f54aadd045cd373e2d6b4abc48ac081a27b4906bb8cc4de8364286d6b7898d","observation_id":"4df30656-e1a1-475f-a3d5-2ea990b54a9c","resolution":{"observed_at":"2026-08-11T10:41:14.168729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:14.145559Z","title":"Generalization and representational limits of graph neural networks","venue":null,"work_id":"611656fd-8b75-4750-ab16-8e7c20c243c1","year":2020},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.185429Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:7f291b5f8077f43bb72fa2e254d3f82bc3a80ae0dc40223c3640172fa377e8df","observation_id":"1a8614cc-f5ea-4354-b402-35e2206dc27a","resolution":{"observed_at":"2026-08-11T10:41:14.151128Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:14.128385Z","title":"Gpt4graph: Can large language models understand graph structured data? an empirical evaluation and benchmarking","venue":null,"work_id":"16f52af5-051a-436f-8652-16ca2ce33cef","year":2023},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.190225Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:b7e77981e7ed9eeec84ca24ffa41374b276684ee6df44bd2bc34300d4956d523","observation_id":"658952bd-b58a-472a-b62f-931285c82af7","resolution":{"observed_at":"2026-08-11T10:41:14.133606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:14.108747Z","title":"Mirage: Model-agnostic graph distillation for graph classification","venue":null,"work_id":"1968c0e7-3518-4894-8d67-dbd951fe5de9","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.195572Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:a7b1ae62d4ad7ca276352132d574b4ba83d9b5f1dc89dea2e3a627b98ede13ad","observation_id":"b85578f2-cc70-4b31-a358-91155852ffae","resolution":{"observed_at":"2026-08-11T10:41:14.114083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:41:12.200549Z","title":"Inductive representation learning on large graphs","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.200549Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:40171635573b6b63a2fa6ef960a957001b412a01b91413b05126db8fa4d3ca12","observation_id":"0652256e-41d7-4f47-ba42-e1e4104da3fe","resolution":{"observed_at":"2026-08-11T10:41:12.200549Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:14.078681Z","title":"A., and Jin, W","venue":null,"work_id":"837e2501-e0f2-4064-9eb6-2c8c7595b65a","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.205947Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:c1ecd5dd62ba1005805efb1ba62e174267e07c0013caadbde4b01ed414723762","observation_id":"69264fcd-26e1-4cfc-8c2f-65c6d30e9cb5","resolution":{"observed_at":"2026-08-11T10:41:14.083872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:41:12.211750Z","title":"Masked autoencoders are scalable vision learners","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.211750Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:399a25d34745e6312f16a33bd5a09c11bfbf63927ece493ffa15933b1b994a43","observation_id":"ed69704c-8728-4e82-add4-8515c83bb71d","resolution":{"observed_at":"2026-08-11T10:41:12.211750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13630","last_updated":"2025-01-20T15:12:02Z","snapshot_observed_at":"2026-08-10T22:17:51.072949Z","submitted_at":"2024-02-21T09:06:31Z","title":"UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13630","snapshot_observed_at":"2026-08-11T10:41:12.216555Z","title":"and Hooi, B","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.216555Z"},"links":{"cited_paper":"/paper/2402.13630","citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:2acb3560e997e8ee2775e900244867ed8f77598a8e906386534918c8eb60addd","observation_id":"2585c4f0-3945-4e38-820a-e456af8697b4","resolution":{"observed_at":"2026-08-11T10:41:12.216555Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:14.043167Z","title":"Graphmae: Self-supervised masked graph autoencoders","venue":null,"work_id":"98cf3d98-ab58-42c4-83d9-dc5546c9b3f6","year":2022},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.222484Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:9efaa8a0139c5fbffce6c863637e0ca19d54d807a06e6f58cf458f53f01c32fe","observation_id":"aac798f1-bb39-4bb0-aa9e-a38f017814ae","resolution":{"observed_at":"2026-08-11T10:41:14.052051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:14.018090Z","title":"Strategies for pre-training graph neural networks","venue":null,"work_id":"5f7801a0-3aa6-45f9-bfe8-bea73547aa82","year":2020},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.228086Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:e9ce4afe21973178c0887acda100ca095e01074b3e97194009b33ad52e2e5764","observation_id":"2b801f47-6de0-47da-bf9a-343e1ff8a893","resolution":{"observed_at":"2026-08-11T10:41:14.026440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.995320Z","title":"Pre-training graph neural networks for generic structural feature extraction","venue":null,"work_id":"9e75a77a-0d1a-49ae-9a29-056f232c821f","year":2019},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.233351Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:2918ac2e90d687995e872fd59eade0f7ef26517a7b370b19c4caa6893d9471a8","observation_id":"7e107451-df98-4688-a126-727224908943","resolution":{"observed_at":"2026-08-11T10:41:14.002393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.973939Z","title":"S., and Leskovec, J","venue":null,"work_id":"20a1dcbd-56d0-4b49-83b7-5d47ca1f6a41","year":2023},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.238728Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:061296fbe53dc1322c9b0b1b217b64b2611221fdaeb17f8e230ea5ff8cc63f6f","observation_id":"3da204ca-d068-4724-b4bd-084f9385a094","resolution":{"observed_at":"2026-08-11T10:41:13.979290Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.957400Z","title":"Self-supervised learning on graphs: Deep insights and new direction","venue":null,"work_id":"2aea5d8f-b16d-4fc9-bea1-c631c030c52d","year":2020},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.243813Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:8b1a9247313fd46ca84503cb6f96f3dd8020151f7396c1ce117f0b3a6b1b8ecd","observation_id":"9ab8d4bd-ce8f-44d3-8cdf-83223e7fb045","resolution":{"observed_at":"2026-08-11T10:41:13.962263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.941260Z","title":null,"venue":null,"work_id":"346584f8-5308-43bf-a092-440ded516c8b","year":2023},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.249610Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:9d7d88841ccc148b774db7264573a15e2e729071f0c66ff2510b68a5bd4b82f4","observation_id":"322b3c57-ef31-4576-9448-f803b9a4ae45","resolution":{"observed_at":"2026-08-11T10:41:13.946437Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.923938Z","title":"Multi-task self-supervised graph neural networks enable stronger task generalization","venue":null,"work_id":"2a2a0ecc-a4b1-4708-8914-d5cb2c6f0a78","year":2023},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.255091Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:364677e76866403374c44d39b329f2d3b388dd18d28137f928e2fbcfd03d8942","observation_id":"c56f4196-a62b-466b-bc0a-8e061851ee6f","resolution":{"observed_at":"2026-08-11T10:41:13.929494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:41:12.259544Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.259544Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:753d56fc69a39015ae834ee2c854f9142565c1cdf3cabd96e545f34218206c41","observation_id":"b7ff22ec-a8be-47ee-a156-cf30c975e115","resolution":{"observed_at":"2026-08-11T10:41:12.259544Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.891273Z","title":"On the transferability of spectral graph filters","venue":null,"work_id":"ede76f20-0f8b-4bd1-af87-cecf2bf8b775","year":2019},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.264222Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:8bf291095dd39ceafa09cfe82ab91ffe15ef88ee718ed597f2904e914954580e","observation_id":"69ee22dd-a4af-4d6d-84a5-5dee8c2bfc6b","resolution":{"observed_at":"2026-08-11T10:41:13.897429Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.873933Z","title":"Transferability of spectral graph convolutional neural networks","venue":null,"work_id":"4db37d86-70c7-4936-ae91-8d110eb93774","year":2021},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.269147Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:9ac7a34918921e272d05cb47f3b9061e11b73812a5f3a7befa5732ab64ca3198","observation_id":"a1f872a2-a46e-48a5-97c5-4d0d049c959c","resolution":{"observed_at":"2026-08-11T10:41:13.879815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.857712Z","title":"X., and Li, J","venue":null,"work_id":"480bbd2f-0863-4c3d-a7a9-7019b57d559a","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.273751Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:8323c2a8ebd25349e51c414d757c35787318f55b87a4eb0993ae7216accdbb34","observation_id":"7e7e66f6-780a-480b-a8e0-07be78c360a0","resolution":{"observed_at":"2026-08-11T10:41:13.862604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.839567Z","title":"One for all: Towards training one graph model for all classification tasks","venue":null,"work_id":"b6214a5b-aaa2-4eaa-826f-03e86283a474","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.278862Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:0a7653489d3efff1ada3ac44634cf10863c60c3dc9e31ce33f3401281fecd93c","observation_id":"e7c9463e-aaf8-4c6a-ac6a-0090c7dc0cff","resolution":{"observed_at":"2026-08-11T10:41:13.845366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.819336Z","title":"Graphprompt: Unifying pre-training and downstream tasks for graph neural networks","venue":null,"work_id":"9094486d-c3a1-4860-a3af-c3769c661f4d","year":2023},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.283778Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:c662687928e2c448e8d395a2cfa59554971c36871c1549a5c793fb7f92c65bdd","observation_id":"1e573ee0-0714-4057-8b87-a618bf4966f1","resolution":{"observed_at":"2026-08-11T10:41:13.826709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.797559Z","title":null,"venue":null,"work_id":"366c7491-6c61-435b-a148-3372d96aeb1d","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.288906Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:a63a08725ebbcc330c7a1af5619191b96114fe55639d9bfc4c86b5d196d42175","observation_id":"3212d489-5337-48f9-9ca4-47c7b2449b5e","resolution":{"observed_at":"2026-08-11T10:41:13.803582Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.775060Z","title":"Revisiting heterophily for graph neural networks","venue":null,"work_id":"6f35e215-780a-4105-a3e6-51e741261dfc","year":2022},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.294004Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:9691567981410abb6a4ae341071b4789432391be7251d4bf7b1728f4618bb855","observation_id":"6279150e-cc1c-4b05-9dfe-47c7d1b4962b","resolution":{"observed_at":"2026-08-11T10:41:13.780730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.755315Z","title":"Hypergraph contrastive learning for drug trafficking community detection","venue":null,"work_id":"b49636de-8dae-4c8c-af18-18a5fca278f4","year":2023},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.299588Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:dd6fefa09f1ac4b32c9d337c2a3819f640345b7a6f62cd5f54581a40d943a6fc","observation_id":"407d560b-1a16-4fa7-bec5-063fdec3a54b","resolution":{"observed_at":"2026-08-11T10:41:13.761665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.733010Z","title":"Is homophily a necessity for graph neural networks? In ICLR, 2022","venue":null,"work_id":"3e84f997-e6fc-491b-9066-0e7b18441931","year":2022},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.304527Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:f501a0d95a24b9666bd8760761c685fde67097657c416065b319b94bb15de760","observation_id":"9efa5c41-0206-40f2-bf2b-67f5be548b56","resolution":{"observed_at":"2026-08-11T10:41:13.739557Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.712225Z","title":"Graph foundation models are already here","venue":null,"work_id":"a085e5a9-8673-4a34-9ee2-82b5732d3b15","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.309461Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:7c755f290bb33bbfaeac7bf1f6e35b7fa68469ce124bc527677873d60e1cd8ab","observation_id":"37befae0-4d89-47ed-96ef-7e35e0903fba","resolution":{"observed_at":"2026-08-11T10:41:13.718923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:41:12.314300Z","title":"L., Lenssen, J","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.314300Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:01a1ec373cd98845f774f57192261c0f88cad9cff78be55c2f83968e091dfe75","observation_id":"e8d26391-c6a6-417d-8d06-be697ddc63b6","resolution":{"observed_at":"2026-08-11T10:41:12.314300Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.655734Z","title":"Wl meet vc","venue":null,"work_id":"6bb1b2d5-b6c7-4c5b-b3eb-a05e07d7f60a","year":2023},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.319537Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:6a7a205b0d99adf7a40e6aae82304b7953ece884b185cbced54b3eb13596318c","observation_id":"228b65ea-73ca-40f1-835f-8d16d3a6b950","resolution":{"observed_at":"2026-08-11T10:41:13.662191Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.636436Z","title":"Towards trustworthy retrieval augmented generation for large language models: A survey","venue":null,"work_id":"8e60aab5-8975-4449-b6cd-08c27385fe16","year":2025},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.324949Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:11ae1854219a78fff0476616ae0b1f277f5f1ef92633922ad81cbc19310ff7c3","observation_id":"8d64bb31-190c-4d46-82e6-ce5cad2bde91","resolution":{"observed_at":"2026-08-11T10:41:13.641484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.614158Z","title":"Dual-level hypergraph contrastive learning with adaptive temperature enhancement","venue":null,"work_id":"23ad0319-8ab0-443a-bff1-fd1442e8563a","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.330361Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:46f817dd8bd73396b22822e78d1b14f20e05e0b68341d0997000313d3d3cab10","observation_id":"a5b94271-27e9-43f5-91be-bd07de921d57","resolution":{"observed_at":"2026-08-11T10:41:13.621682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.593221Z","title":"Adaptive graph enhancement for imbalanced multi-relation graph learning","venue":null,"work_id":"c924fc56-5116-4744-b784-0448e96146db","year":2025},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.336561Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:b27af61d3b732308b2951b0590880f529f60f209cad07978d7819d1142a1203b","observation_id":"0935e41c-2dbe-49cb-9702-8daf0b8e9e59","resolution":{"observed_at":"2026-08-11T10:41:13.599761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.573918Z","title":"Gcc: Graph contrastive coding for graph neural network pre-training","venue":null,"work_id":"77871d87-02e8-44d9-ae4a-44d28ac0006a","year":2020},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.341472Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:171b9d3f2f65df697827a3af5c9e2e2423eb3b79fa5bf7f0b891cc52ed79a40d","observation_id":"a1a7b916-2332-4578-88da-b30ba9513852","resolution":{"observed_at":"2026-08-11T10:41:13.580270Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.554424Z","title":"and Gurevych, I","venue":null,"work_id":"90db471f-5147-43ba-856f-3ea547188dbf","year":2019},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.346652Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:ec584d590519d5c0324580c9def69e8f6f2b00de15c87d427613ac83e83a3a03","observation_id":"9785ae99-17f7-4645-b7b3-6d5be81cc851","resolution":{"observed_at":"2026-08-11T10:41:13.559980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.537868Z","title":"Perturbation bounds for means of eigenvalues and invariant subspaces","venue":null,"work_id":"e1fa88f2-c658-4106-94a3-874aa55d15fc","year":1970},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.352957Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:b3f98162e5bdb5c546d3fc7cf2b164828f54e69be12d710d7c6e4b3a02ac09e9","observation_id":"bc57d5aa-281e-421b-9d44-660e4b317f58","resolution":{"observed_at":"2026-08-11T10:41:13.543445Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.519886Z","title":"Graphon neural networks and the transferability of graph neural networks","venue":null,"work_id":"3a82d268-5881-4ac0-b499-d710d7114c6d","year":2020},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.358300Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:de973fa18f750de28edface749b9bfe15583828b44a8a3ebb9b3f298b2d16b35","observation_id":"10f254f9-01b4-4e2f-aaa4-92c3678ebfaa","resolution":{"observed_at":"2026-08-11T10:41:13.525706Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.500397Z","title":"A., Cubuk, E","venue":null,"work_id":"9d9405d9-461d-4662-895c-3e1fc42ed2a2","year":2020},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.363515Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:2b7770200b3465bd273ad93e21c2221079591aca8626db3f2db7e0adf5cfe866","observation_id":"25e7d77e-c4f2-442b-9963-c457cd11af1a","resolution":{"observed_at":"2026-08-11T10:41:13.507125Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.478989Z","title":"Preference ranking optimization for human alignment","venue":null,"work_id":"07e35b52-e76d-41e8-b696-36738eb1320b","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.368457Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:f9b08a4b2acad103c6863e44e803ba5b5c5c222539d2b5253a46afdd15b88e13","observation_id":"4057a49c-4fdb-42e6-8fab-e096fc128fb5","resolution":{"observed_at":"2026-08-11T10:41:13.484773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.462784Z","title":"and Ribeiro, B","venue":null,"work_id":"721cd5c3-0015-474f-8932-9edf75f34aaa","year":2020},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.374545Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:9b21b96eddc87143cce6aa0f24e417f66d16d84deb605d9381a9b533dd45bbbe","observation_id":"1b19830d-0cb5-4405-8fe5-84edb20b0933","resolution":{"observed_at":"2026-08-11T10:41:13.467481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.445366Z","title":"A molecular multimodal foundation model associating molecule graphs with natural language","venue":null,"work_id":"d088bdf5-3d5a-4284-bd65-96997e93841d","year":2022},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.380493Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:e50e20a6f951479e3252e4923a9a01c2e9c3ebd7ccf1f1f5a868a07f8a2fe945","observation_id":"d02ca9c2-9866-4561-ad8e-f8960a5b7180","resolution":{"observed_at":"2026-08-11T10:41:13.450241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.427817Z","title":"Gppt: Graph pre-training and prompt tuning to generalize graph neural networks","venue":null,"work_id":"367e3988-ef1d-426b-9883-74894af4f153","year":2022},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.385725Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:69cb100e35386a9e0ee1e9700c70c41afe6f3f7d0b0b237deb5091487ce4a761","observation_id":"8069558d-6d54-4d92-9f39-49a51f6cab27","resolution":{"observed_at":"2026-08-11T10:41:13.433067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.409349Z","title":"All in one: Multi-task prompting for graph neural networks","venue":null,"work_id":"1d6f13e1-530b-4b2c-b30b-bc1bbda7ceac","year":2023},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.390388Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:f6a2ab325ac0e5a3ff7ce876f62d5a43176712ea6abb9da56a287b322837535a","observation_id":"6e32d439-4422-4bdf-a13c-e6d6ff8269d6","resolution":{"observed_at":"2026-08-11T10:41:13.415382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.393330Z","title":"Fine-tuning graph neural networks by preserving graph generative patterns","venue":null,"work_id":"4098fd86-8a8a-49d8-9942-c2fdd973dab7","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.395002Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:2b221b3e44b056bbdeb9138c62ba5f3b6cb680e539fce78ce5162780d9f1d11d","observation_id":"95969635-6d45-426a-a207-f3d9be42aa6a","resolution":{"observed_at":"2026-08-11T10:41:13.397983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.377153Z","title":"Transductive linear probing: a novel framework for few-shot node classification","venue":null,"work_id":"1118ddd0-f358-4ddc-a3f3-9d188a97179a","year":2022},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.399584Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:2f8be142688b99262ca0b704b35f9b6b9ea98d4d3ab19a7906e19422d74fab97","observation_id":"a4678be6-281c-4a5f-b749-803cc0bb1d2a","resolution":{"observed_at":"2026-08-11T10:41:13.381992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.360536Z","title":"Graphgpt: Graph instruction tuning for large language models","venue":null,"work_id":"b10a4821-bca6-46e6-88b7-f05f44db4aa0","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.403962Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:e394e03b061c00201ade2b899be7ab17a6bffe2414dcdf67fe66160b758eeee2","observation_id":"c6e402fd-8ee9-4b05-a865-8d5f37590d5a","resolution":{"observed_at":"2026-08-11T10:41:13.366148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.342017Z","title":"Galactica: A large language model for science","venue":null,"work_id":"6855230c-7274-479a-8c41-edbd7a155839","year":2022},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.408767Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:fb9554054285b934dd3839c5abaf3f39292bfe5b8562731b7f6e9a04c02cd68c","observation_id":"91e8674d-d1ac-4324-a487-b3ebfbdeef76","resolution":{"observed_at":"2026-08-11T10:41:13.347888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.323478Z","title":"G., Azabou, M., Dyer, E","venue":null,"work_id":"e0ffb87b-9a37-4964-ae55-20f7336f2c89","year":2022},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.415936Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:599de93d5a27443db33e9203320089a3bfe08631ed298bd87690042a79f88857","observation_id":"8c82ba25-2c45-4e74-8dec-ed3af6c9839f","resolution":{"observed_at":"2026-08-11T10:41:13.329572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:41:12.420506Z","title":"Llama: Open and efficient foundation language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.420506Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:d25af5a4a3afbbb934a0a2fbfa8dbd9ef8e250018441bf9d121921187ee8b99f","observation_id":"a8118e91-4f1b-430e-8362-dcbbd7763b83","resolution":{"observed_at":"2026-08-11T10:41:12.420506Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.285277Z","title":"Graph attention networks","venue":null,"work_id":"bf8a72ea-ac11-45a1-9615-35e5dc2a6776","year":2018},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.424953Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:8920c1afad579637a413ba48dc85d5eb7b9e6971e2d5bd347de21f75907967b4","observation_id":"7110fcec-313f-453e-8082-ec01f54a23a0","resolution":{"observed_at":"2026-08-11T10:41:13.291618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.265888Z","title":"Can language models solve graph problems in natural language? In NeurIPS, 2024 a","venue":null,"work_id":"9b144434-d396-450b-8907-2f0fd82e904a","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.429351Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:590875c7dd3a4f7819157ecd61eae739f52d5ba789ee6c928bc4e0681b443c58","observation_id":"988c368c-b0ce-405f-91da-a194c2ed9523","resolution":{"observed_at":"2026-08-11T10:41:13.272060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.249710Z","title":"Graph few-shot learning with task-specific structures","venue":null,"work_id":"0ba92d2c-9d7f-48d9-89bf-17159bafef4f","year":2022},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.433761Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:9a2f0118255ccbd73813c8f669229aa930f6674338b05450d2c7974e2a3e2a07","observation_id":"e5724db6-92ac-449a-8e84-cd1e3a712365","resolution":{"observed_at":"2026-08-11T10:41:13.254896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.231667Z","title":"Task-adaptive few-shot node classification","venue":null,"work_id":"86140304-947b-4ff7-be1a-b614f98e62bc","year":2022},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.438176Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:337ff4c617035be93a1d07d5799cb3791044a3f17c6cddfc6e583b9113ad9cb4","observation_id":"1419eeb8-30b8-4cd9-b50d-3ede451f0bdd","resolution":{"observed_at":"2026-08-11T10:41:13.237836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.203158Z","title":"V., Zhang, C., and Ye, Y","venue":null,"work_id":"5509a2e1-201d-490b-b026-59527581714d","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.442366Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:40abb386da261e505431e6df8d1399c98083a567efd67ca25edee43389354099","observation_id":"668f0ef7-d7d1-4b30-8ec8-5da57549f9ab","resolution":{"observed_at":"2026-08-11T10:41:13.211593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.183040Z","title":"Subgraph pooling: Tackling negative transfer on graphs","venue":null,"work_id":"bafa40af-ff96-4570-998d-c05b9c0c23d4","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.446734Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:8e33d11a8e313dbfe676c076cc961c2225fce9be61d2788195371c48c03f66ff","observation_id":"423f9ea8-0a09-45a1-b2fd-100c99d2be0a","resolution":{"observed_at":"2026-08-11T10:41:13.190510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.166655Z","title":"Can llms convert graphs to text-attributed graphs? In NAACL, 2025 a","venue":null,"work_id":"49039bd8-6d17-46cb-be04-b2bccb923223","year":2025},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.451245Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:31752905972b69c468417294e14c06d2825c28dc783b98efac7962faf33404fe","observation_id":"5027ae82-858b-4b57-814c-f180ebf630e4","resolution":{"observed_at":"2026-08-11T10:41:13.171826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.147962Z","title":"V., Zhang, C., and Ye, Y","venue":null,"work_id":"b61f9884-e3c4-4ba3-adae-472f06d41a47","year":2025},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.455598Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:76a97778817847816434f59765e3bbee87f91b2b76540f4f8afba75bc9f98198","observation_id":"9e2a23b4-3622-4b68-bebd-ceaa1b201a5f","resolution":{"observed_at":"2026-08-11T10:41:13.153470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:13.128411Z","title":"Training mlps on graphs without supervision","venue":null,"work_id":"0d49cdc4-3bf3-411d-8cab-b59287974e3d","year":2025},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.459774Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:4eb149a76064f79bf734742de88ca074ee5846c3b9dd107df7ce59492156ebd0","observation_id":"dc79cb94-3e01-4210-bb9b-006f843e9785","resolution":{"observed_at":"2026-08-11T10:41:13.135108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.963277Z","title":"Y., Guu, K., Yu, A","venue":null,"work_id":"d36a18c7-fef0-42da-9d50-149ec2ebcd53","year":2021},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.464021Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:f431d27adc4e3a4643c2b5683454192fbe87139ccf15f361886651c83420d973","observation_id":"6bfecc74-8f2a-40ad-93c1-4ea56abf533b","resolution":{"observed_at":"2026-08-11T10:41:12.968540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.946789Z","title":"From coarse to fine: enable comprehensive graph self-supervised learning with multi-granular semantic ensemble","venue":null,"work_id":"1a59b9af-2cc7-4a4a-b1f2-d944a446b553","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.468517Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:427d45f526091ce1d5e7a56d1868373d043683fd29295c85de324baaec22e7c8","observation_id":"b2236820-6159-4407-8bb2-21729612da64","resolution":{"observed_at":"2026-08-11T10:41:12.951963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.929075Z","title":"and Huang, C","venue":null,"work_id":"d1ba3e65-985b-41a5-a6a1-cc4f52c28b08","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.473314Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:62363c2d11b85af169943921d81fe4b2667dcd8611ee59c83e59bec3d3f3a6da","observation_id":"4155c7b4-8e42-4629-bc33-b8e7e168248e","resolution":{"observed_at":"2026-08-11T10:41:12.935350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.911155Z","title":"Opengraph: Towards open graph foundation models","venue":null,"work_id":"30e9a3ac-d0de-470e-832e-2c01f5241d9c","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.478428Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:85cc201f1554faacfe7dfacfe139f50d86d2d12dc1fc171c3835f8c527991688","observation_id":"dfcd3c4c-0d50-4acf-8ea8-1e6600ca3b53","resolution":{"observed_at":"2026-08-11T10:41:12.917261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.894701Z","title":"M., Raghunathan, A., Liang, P., and Ma, T","venue":null,"work_id":"f35b79d1-ddcf-4c7b-b488-64e6e2c4d558","year":2022},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.484487Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:6d430987ca8a332a54dddd2e47e68111e88a00879d1f5016a98a08de7c814613","observation_id":"8840aa6c-5824-4043-a8b8-c7b9c6c2a053","resolution":{"observed_at":"2026-08-11T10:41:12.899327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:41:12.489922Z","title":"How powerful are graph neural networks? In ICLR, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.489922Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:37416e08edda3102c7862967e67fb84268cbdcb7866b1be2749df41de87b43d7","observation_id":"7deaf86f-b95d-4cf9-8803-ef47f53cca9a","resolution":{"observed_at":"2026-08-11T10:41:12.489922Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.866019Z","title":"Text-free multi-domain graph pre-training: Toward graph foundation models","venue":null,"work_id":"356a992a-f438-4945-ab3f-bbef242226b7","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.498532Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:2cda563e56d206dccd5d3d9c853a43ec164e1d84d7f5b72dbaef5949fedf74c3","observation_id":"d53f5bff-ec3b-4a70-91ed-a80b1e098b83","resolution":{"observed_at":"2026-08-11T10:41:12.870988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.851664Z","title":"Florence: A new foundation model for computer vision","venue":null,"work_id":"e8d47671-d52e-4f98-a99c-32f6e79caadb","year":2021},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.503611Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:a609e9270787101c4c575cd11b46884dc16fd279ac1a543654a259ed583620f8","observation_id":"73bac78b-93d2-4b0e-ab38-8c218f9d587f","resolution":{"observed_at":"2026-08-11T10:41:12.856257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.836040Z","title":"A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals","venue":null,"work_id":"da882aba-639b-4ee0-880c-c6deb1f9adb4","year":2022},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.508797Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:88a6eebc9d1805022cf7ea82cea05e0f5a7bdeb234881bd98f4c5aa20ec8f026","observation_id":"81e43eb0-1166-4d3f-bbcc-45edfc28d6e1","resolution":{"observed_at":"2026-08-11T10:41:12.841188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.819701Z","title":"Beyond weisfeiler-lehman: A quantitative framework for gnn expressiveness","venue":null,"work_id":"500afaeb-b41d-43ec-a20a-1eb3ede17b21","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.518892Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:aac50ba6dac4f84ebd0dfcbca611d81c23e7da3389a13372e9b2f1a49dbb756a","observation_id":"09a0b6af-2c9c-4d1d-8969-eaf1e6cbd5ca","resolution":{"observed_at":"2026-08-11T10:41:12.824922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.802830Z","title":null,"venue":null,"work_id":"8c825d08-d3fb-4a16-95f6-fc0a41a194f3","year":2019},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.524364Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:3f98a1fd3d18c70a6f503bc8b7b4314ad2741cf817cadb5c3a3677244edc051f","observation_id":"1f5764cf-6340-4b09-9a80-ffc631045015","resolution":{"observed_at":"2026-08-11T10:41:12.808174Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.786161Z","title":"Dtgb: A comprehensive benchmark for dynamic text-attributed graphs","venue":null,"work_id":"c2f15ca5-7505-433f-bd51-b773ed67b9dd","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.530539Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:52028ffb2a993c99b7ba46bbe657e301fbd9eb350384c1b9c7974060638527ef","observation_id":"a9bc3320-04cf-42b0-be5a-06f8253e72e2","resolution":{"observed_at":"2026-08-11T10:41:12.791722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.769009Z","title":"Labeling trick: A theory of using graph neural networks for multi-node representation learning","venue":null,"work_id":"34510603-d13c-4274-8c4a-cdbc356d823d","year":2021},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.536083Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:d2d9f5306774ebedb4afb88fe71d894e44f5c8b001b367c8f3d23fb026293bea","observation_id":"cdd83184-8977-4e9a-bc50-cfaed915b50e","resolution":{"observed_at":"2026-08-11T10:41:12.774136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.750983Z","title":"V., Zhang, C., and Ye, Y","venue":null,"work_id":"01a62e09-5b01-478d-869b-de6a49d0a981","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.541395Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:384ced277611c2919f40ef950eb26f3993fb8c175a20b4655f1816e173627a39","observation_id":"f63a1393-d3d5-4163-b245-2468db203917","resolution":{"observed_at":"2026-08-11T10:41:12.756802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.732774Z","title":"Gimlet: A unified graph-text model for instruction-based molecule zero-shot learning","venue":null,"work_id":"5256bd42-9780-4ca5-9622-a3a02a7f1547","year":2023},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.546370Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:5ceaac1aa4b3d90ff37b127b4ba081ae92dfbffef9ad480dda8faef1a4b304dc","observation_id":"e93d0700-0e93-46ef-8ec9-d653fa9d0564","resolution":{"observed_at":"2026-08-11T10:41:12.737747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.716589Z","title":"All in one and one for all: A simple yet effective method towards cross-domain graph pretraining","venue":null,"work_id":"57dfbf6c-ca9c-4bd0-8057-5c4ff32bc437","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.551581Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:231975b081b0b30b622ddfe774d34e0c1063900a29ecc1cc5a781086a8bad0b8","observation_id":"ea27b729-2784-4c0f-9625-6b025f73c246","resolution":{"observed_at":"2026-08-11T10:41:12.722047Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.698814Z","title":"Graphany: A foundation model for node classification on any graph","venue":null,"work_id":"31a082db-9605-4ed1-8d3d-99ad9374bce9","year":2024},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.556605Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:7434a7ba466e782aef625d29d43c8dc66fa8b09d7f199ffa1e74ef5c8480de26","observation_id":"52746894-49cd-4585-9522-940318e98a81","resolution":{"observed_at":"2026-08-11T10:41:12.704371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.680147Z","title":"Transfer learning of graph neural networks with ego-graph information maximization","venue":null,"work_id":"064da0bf-724c-41b1-837f-3b726cf69abf","year":2021},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.561242Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:3d24f334e2d56c42b6a9a53e7f5a3ecd9d4834ab3203493d61d4fb2c789cf25d","observation_id":"3d5a7346-bcbc-49e0-8424-e6b65c403848","resolution":{"observed_at":"2026-08-11T10:41:12.687617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:41:12.658494Z","title":"Deep graph contrastive representation learning","venue":null,"work_id":"bcc11b18-a49e-4f01-8440-296627d4e3e2","year":2020},"citing_paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","version":3},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-08-11T10:41:12.565965Z"},"links":{"citing_paper":"/paper/2412.16441"},"observation_digest":"sha256:2721f173f01eb845a3d63d657d1e9a883162feacd4521b0cdbd5a02af8038d7f","observation_id":"b199823a-4a78-416e-b421-0c69abaddc45","resolution":{"observed_at":"2026-08-11T10:41:12.667039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.16441","last_updated":"2025-05-25T14:58:07Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T18:35:08.192783Z","submitted_at":"2024-12-21T02:07:43Z","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees"},"reference_resolution":{"displayed":89,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":0,"verified_fuzzy":70},"total_outbound_references":89},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 89 of 89 outbound references and 0 inbound Pith citation observations for arXiv:2412.16441."}