{"as_of":"2026-08-14T23:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:678da3e28268482b5c04326a8353d1117dff9810c5509cd1810246c3fa2d811c","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T13:11:51.029802Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:50:40.024578Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T17:50:40.437920Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"cited_work":{"arxiv_id":"2412.13442","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.13442","snapshot_observed_at":"2026-08-06T17:50:40.437920Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","venue":"cs.LG","work_id":"3ae522a5-858f-40f8-8024-231292833bf5","year":2024},"citing_paper":{"arxiv_id":"2507.09805","last_updated":"2025-07-13T21:41:42Z","snapshot_observed_at":"2026-08-12T23:27:56.309182Z","submitted_at":"2025-07-13T21:41:42Z","title":"Federated Learning with Graph-Based Aggregation for Traffic Forecasting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T17:50:40.024578Z"},"links":{"cited_paper":"/paper/2412.13442","citing_paper":"/paper/2507.09805"},"observation_digest":"sha256:7541cf546460aa85eda38592e72539724025b5ee01ca0dd34928f01ef6c9d700","observation_id":"0a75a33a-343b-45cd-aed1-20262335f9fb","resolution":{"observed_at":"2026-08-06T17:50:40.442329Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.13442/citation-record","integrity":"/paper/2412.13442/integrity","json":"/paper/2412.13442/citation-record.json","paper":"/paper/2412.13442"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-13T11:38:10.906031Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-11T13:11:50.598954Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.598954Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:e1a369a335259ea668a4025a416549c123eaa908ac53d32e6d2b8da4b3bcb223","observation_id":"86b08319-c460-4741-bb65-29e1ff678e28","resolution":{"observed_at":"2026-08-11T13:11:50.598954Z","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":"2026-08-11T13:11:52.628265Z","title":null,"venue":null,"work_id":"6b8c7be5-5964-4604-beeb-539bdb234354","year":2024},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.608312Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:812fe2235e788cbde02cc52743f1cdc6899b4a393d0ee8df861d40dc7a10cdd6","observation_id":"221a345a-e00c-465e-bd83-8caf7d3a0b03","resolution":{"observed_at":"2026-08-11T13:11:52.638427Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:52.589141Z","title":null,"venue":null,"work_id":"dcf29463-a57f-4c67-b0c9-83485fcf82b6","year":2023},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.617766Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:1df06b675e4561b0923401481358d900e797e283ca3e074ef538d4849fc45054","observation_id":"b01c5f4d-f224-4631-9529-1604973a98a6","resolution":{"observed_at":"2026-08-11T13:11:52.600337Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:52.545906Z","title":null,"venue":null,"work_id":"39a9a1ad-b0a3-47de-a0eb-a1f5c7e48317","year":2024},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.631018Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:489206273e7d5c70a5d37b86b6aae3e6a4260e7ad6514a135db20ecc7c551fd8","observation_id":"6ebe14a6-7c41-4da5-a9cb-5e348d01e024","resolution":{"observed_at":"2026-08-11T13:11:52.557573Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:50.638126Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.638126Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:9838091367ce6426cef7e99a05c192d290d93e2c769611fb7563ee4886a265d6","observation_id":"b8c494ca-d21b-4d5d-b0aa-862ab590960d","resolution":{"observed_at":"2026-08-11T13:11:50.638126Z","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":"2026-08-11T13:11:52.508820Z","title":null,"venue":null,"work_id":"e287dc3c-52dd-4aa7-bd4f-760b0a26a9e8","year":2024},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.664262Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:3bee6074025dea0def8632b48f9881f0e054a1e01aeb72c0a315e42b4e4929ce","observation_id":"b4c31b3f-2933-4d0d-84a4-651300861379","resolution":{"observed_at":"2026-08-11T13:11:52.514568Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:52.490218Z","title":"G´ orriz, I","venue":null,"work_id":"32e1aef9-270f-4e3b-a02e-decc9b64898d","year":2023},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.680450Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:237f9efcaebbbe42b1a81789e596330cdea4c92169d89a322a31a1a23ff1eb83","observation_id":"9743661e-cb41-4673-adf9-ba66ad3649e3","resolution":{"observed_at":"2026-08-11T13:11:52.495715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:50.704272Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.704272Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:ab24fc8cb20e2b2359bcb869c18cbe49dfc1f627451d1a80659c12bb6bea55e5","observation_id":"63a8a390-9b60-442f-b21c-8267cfb6dc77","resolution":{"observed_at":"2026-08-11T13:11:50.704272Z","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":"2026-08-11T13:11:52.450275Z","title":null,"venue":null,"work_id":"19688884-52e1-4195-8336-4c4d4122b3a2","year":2024},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.715006Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:91033b722ab35f4447b2fbb8504ea932fef6337a5b565397e6b858385636d1bf","observation_id":"67c4436a-5d49-481e-b32d-a5f459ad9e08","resolution":{"observed_at":"2026-08-11T13:11:52.456563Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:52.412834Z","title":null,"venue":null,"work_id":"0cbac47d-4da2-4c01-b05e-ebb0a08f6f41","year":2025},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.727220Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:60aacea6a6613a30e09fec9faec15c12c847ca8f5d58e12b4d670dd6425dc625","observation_id":"d9e8d264-0ec8-4093-ad0e-38ee8d04781f","resolution":{"observed_at":"2026-08-11T13:11:52.427478Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:52.379415Z","title":"Zhang, L","venue":null,"work_id":"68693619-8741-4fee-a874-510619c0860b","year":2024},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.736329Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:697bc8f0aa78c97e7b2d9b6f8a036c550d6cd4933d523565def165c14e263177","observation_id":"7f05040f-b39a-44ef-8223-a303bc5d002b","resolution":{"observed_at":"2026-08-11T13:11:52.388659Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:52.346886Z","title":null,"venue":null,"work_id":"51393b24-b5cc-44a5-998d-20651f91bdd2","year":2023},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.746105Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:5b0f3262519a989981b23a82eb256ac9cfaf8c8948f54821cd03a35a79c2e52e","observation_id":"b7d037a5-b6f6-4e48-9364-ce331574788d","resolution":{"observed_at":"2026-08-11T13:11:52.356559Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:52.313579Z","title":"Paz-Ruza, A","venue":null,"work_id":"e3383cd3-b2ba-4333-a556-6666a9535454","year":2024},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.761003Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:301f16523adeeaaa3dc94a468969d77b337ea9371398a5b3b85eed0e35fc7591","observation_id":"1a21bd74-666c-4def-9f2b-d5fb9759128e","resolution":{"observed_at":"2026-08-11T13:11:52.322159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:52.277118Z","title":"Himeur, A","venue":null,"work_id":"61b5bbe6-d4f1-43d3-9fae-83cbdef05300","year":2021},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.768716Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:db8324d8aec3225aa93599328bf2ec7be8def41165848fb2639e90b0c7964ff2","observation_id":"32075490-3ccc-4171-9151-2d322ef16216","resolution":{"observed_at":"2026-08-11T13:11:52.284653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:52.255406Z","title":null,"venue":null,"work_id":"35615a80-fb79-49e6-b519-b972cc75a698","year":2025},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.776799Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:39ffd71859765b4ad89b3d5c62f4c4cfada7a5b3572fe251890feb81caa44123","observation_id":"2ac58cbe-9c99-41e3-b384-b7c79ac4ce30","resolution":{"observed_at":"2026-08-11T13:11:52.260988Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:52.212038Z","title":null,"venue":null,"work_id":"1a7977a7-a2af-4651-9bae-09a9ef6b18a6","year":2024},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.787361Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:2a5aabe72d5287521d67fb034d71ad12d9c48118c6353d6625c74d25b57630ab","observation_id":"bab465b3-3be9-4872-89f5-57fce1faffb7","resolution":{"observed_at":"2026-08-11T13:11:52.223766Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:52.184013Z","title":null,"venue":null,"work_id":"ce06d6f2-930c-4db3-be91-c7954cae0c93","year":2023},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.795235Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:8c3b5f494bf396b76caf58562c5c59f882d99cc1aa3824daee414208564bef1d","observation_id":"d85bb0a2-3c7a-471d-94bb-aa2aaeb285d7","resolution":{"observed_at":"2026-08-11T13:11:52.193572Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:52.161366Z","title":null,"venue":null,"work_id":"a8811b43-ee36-4e58-ac8b-751a17bbe446","year":2021},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.805092Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:2dce1d04baf028c03f95f29ff6543ad8868ddf15b418ab9a47d6e741e447efbf","observation_id":"feb181e0-fe0e-45cf-ba72-70631efbf651","resolution":{"observed_at":"2026-08-11T13:11:52.168205Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:50.810422Z","title":"McMahan, E","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.810422Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:98ad940f0fc456d88a6a809220030a5bcf580637522c75e42638c2e0a1b60fda","observation_id":"1dcf49e5-28ba-4842-806f-0d7fb809f62b","resolution":{"observed_at":"2026-08-11T13:11:50.810422Z","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":"2026-08-11T13:11:52.111994Z","title":"Sabah, Y","venue":null,"work_id":"8b560e9e-7123-4808-bdde-a1bf972f3597","year":2024},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.818437Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:d4259d75e34859d4645765370b2274db8fd449c1b82b8abc0c6dc582d14dfee6","observation_id":"8df88c41-0f47-4279-88b2-f13d04d8b039","resolution":{"observed_at":"2026-08-11T13:11:52.118803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:52.084054Z","title":"Huang, D","venue":null,"work_id":"602fcc98-c3f2-459b-bb73-e6dce06a77f7","year":2024},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.833330Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:e517dfbfdaa85fee5ba55523ef51b6f491b32c4998a9cc49aaee43e4a9fb3ccd","observation_id":"4faa1f9f-7bae-40be-b109-3385f71f1a48","resolution":{"observed_at":"2026-08-11T13:11:52.092199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:52.049951Z","title":null,"venue":null,"work_id":"ffc6f5b2-73ab-48ff-b384-95facd7b2aeb","year":2023},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.843606Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:5d4ebc46af8d50d3b7a25c730c9d07100a57cc71a580e225468ff532f4abeb6d","observation_id":"22b98073-fdf4-4d19-a2b2-dc0752dfd9f7","resolution":{"observed_at":"2026-08-11T13:11:52.065004Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:51.817512Z","title":"xuan Hu, C","venue":null,"work_id":"3f077ca0-233a-40b0-bb64-edde6604593d","year":2024},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.853621Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:e2cc846e1f9fbf22ffc68c930c0eb5ee3a0f8bd584b689bc98bd78a213891652","observation_id":"205de1bd-2c35-4bd6-9125-6aa35bc3601f","resolution":{"observed_at":"2026-08-11T13:11:51.823498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:51.797325Z","title":null,"venue":null,"work_id":"107f28b7-ebad-4f58-a667-8f9b81b3df1d","year":2021},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.860901Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:1eff307958c85814ae40b890d1018594f6affc3158fbc85aba5cf09c8fa61581","observation_id":"c82a8819-6afd-4a93-80f5-51294098c163","resolution":{"observed_at":"2026-08-11T13:11:51.802401Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:51.777414Z","title":null,"venue":null,"work_id":"da96e83d-0236-4a49-9b2f-d27b1e3ccdb9","year":2024},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.868916Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:6c5019bde8c143713e86a4c16e88e593fcb1cca4514fe70b68d0b8eb36200576","observation_id":"53735265-dcab-4242-90fb-6d617605ba1d","resolution":{"observed_at":"2026-08-11T13:11:51.785670Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.06378","last_updated":"2021-04-09T16:21:59Z","snapshot_observed_at":"2026-08-11T19:19:26.066613Z","submitted_at":"2019-10-14T18:49:20Z","title":"SCAFFOLD: Stochastic Controlled Averaging for Federated Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.06378","snapshot_observed_at":"2026-08-11T13:11:50.881081Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.881081Z"},"links":{"cited_paper":"/paper/1910.06378","citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:d433f84d9f6fca07c4bc8cb808a113e9879d95c39fecb7927ddb549b7134561f","observation_id":"865957ac-c17e-4037-ae83-7208752818e5","resolution":{"observed_at":"2026-08-11T13:11:50.881081Z","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":"2026-08-11T13:11:51.752817Z","title":"Collins, H","venue":null,"work_id":"d846d859-d74a-4fd6-93be-dbb7ca296760","year":2021},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.891105Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:bd2281424ada0d90075faf42d8b269a71a3ad09060b1da11135d0b18d1f7391d","observation_id":"ab384360-845e-4e71-9776-b881371ffc15","resolution":{"observed_at":"2026-08-11T13:11:51.759724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:51.721281Z","title":"T Dinh, N","venue":null,"work_id":"ac92ee08-85ff-4fe2-a727-c05117cb3524","year":2020},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.901566Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:26814e9d647f26bb188be7162856fd06bd3577e51be431214d682da9aa101186","observation_id":"292db15f-b69e-4d4a-a6af-645f544de36c","resolution":{"observed_at":"2026-08-11T13:11:51.726579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:50.907788Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.907788Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:f78c2a20a98bb8b9f89464870b392f9923f4b8c062b2176fafd1d87aead1ff5c","observation_id":"7864eb76-8eb4-40c8-abac-dc7bd362d686","resolution":{"observed_at":"2026-08-11T13:11:50.907788Z","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":"2026-08-11T13:11:51.677023Z","title":null,"venue":null,"work_id":"53c9f683-42ed-4036-aa57-d036a761f323","year":2022},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.915857Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:dc870b752069831c5fc2acab9a372f9ff59bb93cc484f6c8c023f8f2cddb43ed","observation_id":"c3bc7643-770e-4913-ab08-edf6b98b2e5f","resolution":{"observed_at":"2026-08-11T13:11:51.683724Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.11051","last_updated":"2023-02-21T23:09:45Z","snapshot_observed_at":"2026-08-13T12:39:52.582274Z","submitted_at":"2023-02-21T23:09:45Z","title":"Fusion of Global and Local Knowledge for Personalized Federated Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.11051","snapshot_observed_at":"2026-08-11T13:11:50.921518Z","title":"Huang, L","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.921518Z"},"links":{"cited_paper":"/paper/2302.11051","citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:8cdf728f6232cc173c634221b94c6485691d8e0081adbd7a5e02af04f4e7ef19","observation_id":"3505d036-2c66-4240-ada2-24ee5b54e7fd","resolution":{"observed_at":"2026-08-11T13:11:50.921518Z","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":"2026-08-11T13:11:51.650852Z","title":null,"venue":null,"work_id":"b42ba276-fbd4-4173-8183-6512221a2936","year":2023},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.929177Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:b0f185ae68f2b4debdcee9d2a4f1a74c5d5320e21478f2e08a836b8a8769d95a","observation_id":"84d1b809-587f-40a8-8d22-411e0cfd9e82","resolution":{"observed_at":"2026-08-11T13:11:51.659129Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:51.611262Z","title":"Mishchenko, G","venue":null,"work_id":"1e9d8e37-6d7c-4794-a78a-9bc5c622aebc","year":2022},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.937060Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:134c8a4b149ce115a2182a471d3913794777725e3cf79a04d38e669b514f5b79","observation_id":"50c539b9-7bfc-46c0-b693-d64b5f0f8a89","resolution":{"observed_at":"2026-08-11T13:11:51.624311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:51.588151Z","title":null,"venue":null,"work_id":"56b3d626-885e-40e1-872c-158d6554fc26","year":2024},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.942015Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:56c738170d47a042cc4cf1fb4566ffb6d9ef01492316f04630e015f533a95794","observation_id":"d54074a4-586c-414f-a393-36a42ec7b30f","resolution":{"observed_at":"2026-08-11T13:11:51.595370Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:51.564653Z","title":"Huang, L","venue":null,"work_id":"742e363a-27de-4d84-943e-a7c81d1c7726","year":2021},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.948694Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:9712b23a79384625ef4c1df30b4e8634db816fc00d6b8aff8a7fd61cfbd07507","observation_id":"c119dde8-0a44-44b2-b11e-70ae040e2ceb","resolution":{"observed_at":"2026-08-11T13:11:51.571450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:51.530209Z","title":null,"venue":null,"work_id":"762fcf6d-5b62-498b-a984-620cea76535b","year":2022},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.955814Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:07b95eee67c2494289c9897cf35e906f17f007f5ab2ede0c5c5c0483174a1aa4","observation_id":"e735e339-bac6-42a3-bd21-c11b72539fde","resolution":{"observed_at":"2026-08-11T13:11:51.539941Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:51.495226Z","title":null,"venue":null,"work_id":"4b48f63f-fa43-4127-a67b-519598468cd0","year":2022},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.960795Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:c7b75ef02acf277db4bf08eb9b97ec7fa28169cdb2f6c08adc17cd1554041c47","observation_id":"e9e97ef5-7694-46e4-bed0-020c8dde7d17","resolution":{"observed_at":"2026-08-11T13:11:51.504972Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:51.461129Z","title":"Ghosh, J","venue":null,"work_id":"0d7f6613-ae07-418d-bdd3-fd1e21e50686","year":2020},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.967379Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:4941513ea4b99860455ca6d6ff9db995d910e0a0ee6917366d8d215ff4880110","observation_id":"34c6df9b-0ab8-4f12-84c7-869d5b8a86dc","resolution":{"observed_at":"2026-08-11T13:11:51.473083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:51.434843Z","title":"Zhang, S","venue":null,"work_id":"28463b75-3eba-41bc-923b-a9437801c9f8","year":2022},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.973619Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:648c15e612d6176bc9c5f3ee885390ae59d688da4f7ed15fcbb75cb2202067c9","observation_id":"cc9b90c0-1aa3-45a3-ab2b-2dbd876d5c08","resolution":{"observed_at":"2026-08-11T13:11:51.444357Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:51.409221Z","title":null,"venue":null,"work_id":"3cf0c0cb-d2cc-40e3-b5b9-9fad987d1397","year":2023},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.979188Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:a31f7b5b5c7e2ee00c80a10955cd66f3d722010d083ed85b545e5f17e979d00e","observation_id":"623ff802-6bc0-4ecd-8832-43376da8259a","resolution":{"observed_at":"2026-08-11T13:11:51.419685Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:51.380399Z","title":null,"venue":null,"work_id":"58849efb-1107-42e1-8724-e8a6446e4b34","year":2024},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.984608Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:8b9ab600cc721174e70239072f627ecec8e0c21c8514a478a9e4f518b3720dc0","observation_id":"49e54766-0427-40ca-adb6-ae7bcc13f72a","resolution":{"observed_at":"2026-08-11T13:11:51.388108Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:50.990249Z","title":null,"venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.990249Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:5d7aedc0ec66aeaa0b31baff425fd4e76b16f9de41f3e8f60be9e6764d15cf1e","observation_id":"fc07c289-37c9-42f3-912d-0d47f9a825f0","resolution":{"observed_at":"2026-08-11T13:11:50.990249Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.00818","last_updated":"2019-12-02T14:29:00Z","snapshot_observed_at":"2026-08-13T23:51:40.504761Z","submitted_at":"2019-12-02T14:29:00Z","title":"Federated Learning with Personalization Layers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.00818","snapshot_observed_at":"2026-08-11T13:11:50.999639Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:50.999639Z"},"links":{"cited_paper":"/paper/1912.00818","citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:dca0bce9127e43690bab208ececea26ee0f4b4ad9504b2a6595856bceae937dd","observation_id":"480f4f68-c4bd-40cd-b739-00763d2f82d9","resolution":{"observed_at":"2026-08-11T13:11:50.999639Z","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":"2026-08-11T13:11:51.328716Z","title":null,"venue":null,"work_id":"82b25b0a-dbec-4582-a564-efa6872ed0d7","year":2019},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:51.006678Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:3588efbe3487f7730d893e232fc962b47140fa4865c774696568bd7355a7ddb5","observation_id":"184bc33e-0d7b-4d17-895f-5f5f2869d8f9","resolution":{"observed_at":"2026-08-11T13:11:51.335632Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:51.294865Z","title":null,"venue":null,"work_id":"6e0ae6fc-0435-496c-a05d-78b80b322029","year":2017},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:51.013608Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:d1d6c355eb3a4b39a2eb5e8694790d2ac00e52b0fb19bd97685ae9b528f2e294","observation_id":"86fa6c16-575a-4d4f-a238-751058414a95","resolution":{"observed_at":"2026-08-11T13:11:51.312071Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:51.268855Z","title":"Alistarh, D","venue":null,"work_id":"370ec6c3-b27b-43e4-b746-26e53111b531","year":2017},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:51.020468Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:40ef6a346c542f54796ad2bbfa8d828ed1d027257aaedec2b2d5a9ed203b30e4","observation_id":"2023ef64-6d35-4f72-a0cc-32a79db8d4e7","resolution":{"observed_at":"2026-08-11T13:11:51.277304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:11:51.231908Z","title":"Van der Maaten, G","venue":null,"work_id":"3ff0a4b7-a5fd-4af4-80fb-cf220efd3fac","year":2008},"citing_paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T13:11:51.029802Z"},"links":{"citing_paper":"/paper/2412.13442"},"observation_digest":"sha256:645b64dd037e2f0b78d2ff833d32695e5feaae0c75c423dfd7304d1908900874","observation_id":"5db1ac55-6a32-43b0-9651-8f322217598e","resolution":{"observed_at":"2026-08-11T13:11:51.244504Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.13442","last_updated":"2024-12-18T02:26:07Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T12:34:45.432005Z","submitted_at":"2024-12-18T02:26:07Z","title":"Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":32,"verified_exact":0,"verified_fuzzy":15},"total_outbound_references":47},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 1 inbound Pith citation observation for arXiv:2412.13442."}