{"as_of":"2026-08-10T18:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:25bc461a606c24132b400bc2b2861977389e2bd68117f59400046eedb9d8f4d6","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T17:06:37.382067Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T23:57:29.094466Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2104.07145","last_updated":"2021-09-08T00:22:55Z","snapshot_observed_at":"2026-08-06T20:53:44.444031Z","submitted_at":"2021-04-14T22:11:35Z","title":"FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.07145","snapshot_observed_at":"2026-08-10T17:06:37.382067Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12592","last_updated":"2025-01-23T13:03:43Z","snapshot_observed_at":"2026-08-10T16:58:44.281015Z","submitted_at":"2025-01-22T02:35:20Z","title":"FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Sampling","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T17:06:37.382067Z"},"links":{"cited_paper":"/paper/2104.07145","citing_paper":"/paper/2501.12592"},"observation_digest":"sha256:6fc87725a0cf2fdda635478291de63665d87b14a82233a8ef75933c5db022f06","observation_id":"acc92b24-b518-4bf5-bf26-93889bbd5c7d","resolution":{"observed_at":"2026-08-10T17:06:37.382067Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.07145","last_updated":"2021-09-08T00:22:55Z","snapshot_observed_at":"2026-08-06T20:53:44.444031Z","submitted_at":"2021-04-14T22:11:35Z","title":"FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.07145","snapshot_observed_at":"2026-08-10T05:55:14.309687Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.16888","last_updated":"2025-01-28T12:18:09Z","snapshot_observed_at":"2026-08-10T05:48:52.177160Z","submitted_at":"2025-01-28T12:18:09Z","title":"Secure Federated Graph-Filtering for Recommender Systems","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T05:55:14.309687Z"},"links":{"cited_paper":"/paper/2104.07145","citing_paper":"/paper/2501.16888"},"observation_digest":"sha256:14fba2d1950d4a247edd1dc66e9186feb2156c2bd0e17fb42e0e081d942ce38e","observation_id":"93066199-837b-4798-9e71-8c217218c090","resolution":{"observed_at":"2026-08-10T05:55:14.309687Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.07145","last_updated":"2021-09-08T00:22:55Z","snapshot_observed_at":"2026-08-06T20:53:44.444031Z","submitted_at":"2021-04-14T22:11:35Z","title":"FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.07145","snapshot_observed_at":"2026-08-04T20:45:31.938957Z","title":"Fedgraphnn: A federated learn- ing system and benchmark for graph neural networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.08409","last_updated":"2025-09-10T08:55:23Z","snapshot_observed_at":"2026-08-04T20:45:27.012419Z","submitted_at":"2025-09-10T08:55:23Z","title":"Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T20:45:31.938957Z"},"links":{"cited_paper":"/paper/2104.07145","citing_paper":"/paper/2509.08409"},"observation_digest":"sha256:65dd667745e2aac875ab2fb0c5c19f657cc94b57cc0b8deb1f68d617e962c5ac","observation_id":"7d9f11b9-a01e-4ffb-8930-389c6624fd6c","resolution":{"observed_at":"2026-08-04T20:45:31.938957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.07145","last_updated":"2021-09-08T00:22:55Z","snapshot_observed_at":"2026-08-06T20:53:44.444031Z","submitted_at":"2021-04-14T22:11:35Z","title":"FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks","version":2},"cited_work":{"arxiv_id":"2104.07145","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2104.07145","snapshot_observed_at":"2026-07-02T23:57:29.094466Z","title":"Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, and Salman Avestimehr","venue":null,"work_id":"3a46fd05-53a7-4039-876f-48f9efbec116","year":2021},"citing_paper":{"arxiv_id":"2602.13485","last_updated":"2026-05-20T16:35:42Z","snapshot_observed_at":"2026-08-10T05:57:09.782858Z","submitted_at":"2026-02-13T21:41:52Z","title":"Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-21T12:21:25.151671Z"},"links":{"cited_paper":"/paper/2104.07145","citing_paper":"/paper/2602.13485"},"observation_digest":"sha256:331a2bba444851c8656231af118660f567c36d9efa0a6ddaac45c4112df06de8","observation_id":"c5711e2e-1a21-4f6d-b7fe-f44b5dbeb6cb","resolution":{"observed_at":"2026-05-21T12:24:10.724961Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.07145","last_updated":"2021-09-08T00:22:55Z","snapshot_observed_at":"2026-08-06T20:53:44.444031Z","submitted_at":"2021-04-14T22:11:35Z","title":"FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks","version":2},"cited_work":{"arxiv_id":"2104.07145","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2104.07145","snapshot_observed_at":"2026-07-02T23:57:29.094466Z","title":"Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, and Salman Avestimehr","venue":null,"work_id":"3a46fd05-53a7-4039-876f-48f9efbec116","year":2021},"citing_paper":{"arxiv_id":"2605.08288","last_updated":"2026-05-08T08:15:47Z","snapshot_observed_at":"2026-07-06T23:20:33.816373Z","submitted_at":"2026-05-08T08:15:47Z","title":"UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-12T02:47:38.972072Z"},"links":{"cited_paper":"/paper/2104.07145","citing_paper":"/paper/2605.08288"},"observation_digest":"sha256:b2417d796254e9b273e84c6ee37e788ef9c9c9271b3dffbf2502952e660de417","observation_id":"e20b5f9e-c9c3-420d-95f3-706d9a17824f","resolution":{"observed_at":"2026-05-12T02:51:18.048517Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.07145","last_updated":"2021-09-08T00:22:55Z","snapshot_observed_at":"2026-08-06T20:53:44.444031Z","submitted_at":"2021-04-14T22:11:35Z","title":"FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks","version":2},"cited_work":{"arxiv_id":"2104.07145","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2104.07145","snapshot_observed_at":"2026-07-02T23:57:29.094466Z","title":"Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, and Salman Avestimehr","venue":null,"work_id":"3a46fd05-53a7-4039-876f-48f9efbec116","year":2021},"citing_paper":{"arxiv_id":"2605.11919","last_updated":"2026-05-12T10:35:43Z","snapshot_observed_at":"2026-07-06T23:23:39.723268Z","submitted_at":"2026-05-12T10:35:43Z","title":"STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-13T07:41:51.513934Z"},"links":{"cited_paper":"/paper/2104.07145","citing_paper":"/paper/2605.11919"},"observation_digest":"sha256:fdc4671e2e99111d8192603833c2d62947497a4c71838537bb23eb61ccb1c16a","observation_id":"a926077c-113b-45bf-a4ba-6748880e6b0e","resolution":{"observed_at":"2026-05-13T07:42:30.451841Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.07145","last_updated":"2021-09-08T00:22:55Z","snapshot_observed_at":"2026-08-06T20:53:44.444031Z","submitted_at":"2021-04-14T22:11:35Z","title":"FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks","version":2},"cited_work":{"arxiv_id":"2104.07145","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2104.07145","snapshot_observed_at":"2026-07-02T23:57:29.094466Z","title":"Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, and Salman Avestimehr","venue":null,"work_id":"3a46fd05-53a7-4039-876f-48f9efbec116","year":2021},"citing_paper":{"arxiv_id":"2605.26243","last_updated":"2026-05-25T18:10:20Z","snapshot_observed_at":"2026-08-07T17:09:46.603342Z","submitted_at":"2026-05-25T18:10:20Z","title":"Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-29T23:04:43.841278Z"},"links":{"cited_paper":"/paper/2104.07145","citing_paper":"/paper/2605.26243"},"observation_digest":"sha256:7534f69252dffa9ff62eba5a20960d0ed84c01b95dc3ce06c4ccf0c082a1d015","observation_id":"05dae213-7264-47c0-ad28-de57e846191e","resolution":{"observed_at":"2026-06-29T23:14:02.137342Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.07145","last_updated":"2021-09-08T00:22:55Z","snapshot_observed_at":"2026-08-06T20:53:44.444031Z","submitted_at":"2021-04-14T22:11:35Z","title":"FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks","version":2},"cited_work":{"arxiv_id":"2104.07145","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2104.07145","snapshot_observed_at":"2026-07-02T23:57:29.094466Z","title":"Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, and Salman Avestimehr","venue":null,"work_id":"3a46fd05-53a7-4039-876f-48f9efbec116","year":2021},"citing_paper":{"arxiv_id":"2606.09301","last_updated":"2026-06-08T10:08:52Z","snapshot_observed_at":"2026-08-10T06:26:06.116813Z","submitted_at":"2026-06-08T10:08:52Z","title":"PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-27T17:34:54.567394Z"},"links":{"cited_paper":"/paper/2104.07145","citing_paper":"/paper/2606.09301"},"observation_digest":"sha256:0d74fe8c76aea3e132640492072af4386c873fca644a678ba1cf3f85b5a5971e","observation_id":"61b2d797-e349-4124-adb7-08e86f27423c","resolution":{"observed_at":"2026-07-02T23:57:29.095904Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2104.07145/citation-record","integrity":"/paper/2104.07145/integrity","json":"/paper/2104.07145/citation-record.json","paper":"/paper/2104.07145"},"outbound":[],"paper":{"arxiv_id":"2104.07145","last_updated":"2021-09-08T00:22:55Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T20:53:44.444031Z","submitted_at":"2021-04-14T22:11:35Z","title":"FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2104.07145."}