{"as_of":"2026-08-19T13:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cafef78bde1715eaddf6d19316cc7a3c41c24c6efb4f779d5a00527c5b0dda3b","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":59,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":59,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":59,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":59,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T10:19:33.955710Z","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":"2026-07-04T00:49:18.647891Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"1909.01315","last_updated":"2020-08-25T15:46:13Z","snapshot_observed_at":"2026-08-18T10:54:34.717319Z","submitted_at":"2019-09-03T17:10:28Z","title":"Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-24T00:39:33.038577Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/1909.01315"},"observation_digest":"sha256:67a9ff58ca750a722aea00915e46c8e5f977794ee33f25c3da54ba9d80c05f5a","observation_id":"6bda76c1-54c6-43d2-96bd-be93f6189929","resolution":{"observed_at":"2026-05-24T00:39:33.141199Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2105.14491","last_updated":"2022-01-31T07:20:20Z","snapshot_observed_at":"2026-07-06T11:14:04.454155Z","submitted_at":"2021-05-30T10:17:58Z","title":"How Attentive are Graph Attention Networks?","version":3},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-05-17T02:33:38.686468Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2105.14491"},"observation_digest":"sha256:72e1027225d97c0513292613aa53edc7f34ae5113f618c13defbf4022ebb4be0","observation_id":"295d3210-2a20-4a9c-93e6-4279da46c718","resolution":{"observed_at":"2026-05-17T02:33:38.778840Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-12T19:06:55.737396Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.11074","last_updated":"2024-11-17T13:22:15Z","snapshot_observed_at":"2026-08-18T09:31:39.952083Z","submitted_at":"2024-11-17T13:22:15Z","title":"Spectral Subspace Clustering for Attributed Graphs","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T19:06:55.737396Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2411.11074"},"observation_digest":"sha256:fb8ff9cecd5956829109cfb784eb3de33cc313e7a62ac6b7cd9574706c0b82ae","observation_id":"d9b0e251-8704-45ea-99cf-0217dd57b658","resolution":{"observed_at":"2026-08-12T19:06:55.737396Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-12T11:56:36.714523Z","title":"Open graph benchmark: Datasets for machine learning on graphs","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2411.17676","last_updated":"2024-11-26T18:38:38Z","snapshot_observed_at":"2026-08-17T08:10:14.304425Z","submitted_at":"2024-11-26T18:38:38Z","title":"Instance-Aware Graph Prompt Learning","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-12T11:56:36.714523Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2411.17676"},"observation_digest":"sha256:4a4b06cc27bd98932a51e1c85228297fdba499640378ebdb989b118601720e81","observation_id":"b90e845d-266e-4c63-92a6-321b51be16e0","resolution":{"observed_at":"2026-08-12T11:56:36.714523Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-11T20:00:34.298186Z","title":"Open graph benchmark: Datasets for machine learning on graphs","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2412.06173","last_updated":"2024-12-09T03:09:04Z","snapshot_observed_at":"2026-08-12T22:46:31.066269Z","submitted_at":"2024-12-09T03:09:04Z","title":"Revisiting the Necessity of Graph Learning and Common Graph Benchmarks","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-11T20:00:34.298186Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2412.06173"},"observation_digest":"sha256:9847c91102bf6d937f5d34b5204d7953788d109fa9c5a244000c8a55fd3e4e68","observation_id":"ee54581c-8003-4128-864b-11d3242f6c4e","resolution":{"observed_at":"2026-08-11T20:00:34.298186Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-11T17:02:47.399858Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.10467","last_updated":"2024-12-12T18:37:32Z","snapshot_observed_at":"2026-08-18T09:32:58.586854Z","submitted_at":"2024-12-12T18:37:32Z","title":"MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-11T17:02:47.399858Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2412.10467"},"observation_digest":"sha256:8e89b6bb63afe332d82a32097837663dc88e05b4e0c4a8628badb68ee5671161","observation_id":"02606d9e-2b2c-408a-b3d9-13ee2b5e58ab","resolution":{"observed_at":"2026-08-11T17:02:47.399858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-11T14:18:17.149953Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.12318","last_updated":"2025-03-20T15:13:26Z","snapshot_observed_at":"2026-08-18T09:35:49.687984Z","submitted_at":"2024-12-16T19:35:55Z","title":"Graph-Guided Textual Explanation Generation Framework","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T14:18:17.149953Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2412.12318"},"observation_digest":"sha256:61e94a937967210feccc3a3ec367c42b28591f77af8199ef6dcfa1c3567cf455","observation_id":"353528ec-ac2b-4406-b741-b72514bd890b","resolution":{"observed_at":"2026-08-11T14:18:17.149953Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-11T13:20:06.463887Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.13283","last_updated":"2024-12-17T19:27:24Z","snapshot_observed_at":"2026-08-19T10:15:12.924370Z","submitted_at":"2024-12-17T19:27:24Z","title":"Enhancing Persona Classification in Dialogue Systems: A Graph Neural Network Approach","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T13:20:06.463887Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2412.13283"},"observation_digest":"sha256:d035d05ccd0bf7c2d916a43c1506e5d4e9fab70b30a7f67eb195a8e4300d054e","observation_id":"57542c6c-e579-4d05-94c7-8fc0a6bfc674","resolution":{"observed_at":"2026-08-11T13:20:06.463887Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-11T13:03:17.426897Z","title":"arXiv preprint arXiv:2005.00687 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.13592","last_updated":"2025-06-12T08:23:21Z","snapshot_observed_at":"2026-08-14T00:39:09.191604Z","submitted_at":"2024-12-18T08:15:55Z","title":"PASCO (PArallel Structured COarsening): an overlay to speed up graph clustering algorithms","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T13:03:17.426897Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2412.13592"},"observation_digest":"sha256:acfeecd91201c676bab67d921f0b00b0b4af3425f4cb80301b5ebbd498a7a1b0","observation_id":"c276d391-3d1a-4781-bae7-ed59ce8d4ef9","resolution":{"observed_at":"2026-08-11T13:03:17.426897Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-11T12:24:04.986107Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.14351","last_updated":"2024-12-18T21:34:42Z","snapshot_observed_at":"2026-08-18T09:39:29.210174Z","submitted_at":"2024-12-18T21:34:42Z","title":"Is Peer-Reviewing Worth the Effort?","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-11T12:24:04.986107Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2412.14351"},"observation_digest":"sha256:905e3f4053f325f671824e2d80b580d8ac28cde8357d82d3d1f53224b22f332e","observation_id":"d2f911e7-96d0-4d19-a956-1b68e143a58f","resolution":{"observed_at":"2026-08-11T12:24:04.986107Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-11T10:51:16.178177Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.16144","last_updated":"2024-12-20T18:48:46Z","snapshot_observed_at":"2026-08-14T02:27:42.092097Z","submitted_at":"2024-12-20T18:48:46Z","title":"FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-11T10:51:16.178177Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2412.16144"},"observation_digest":"sha256:da64bbdc196af75bb7ae2b6d2ee4ec183cbf71b870ad3dfa1ccfd3ef66b77b8a","observation_id":"c5b331e1-763f-4188-a5e3-c40e91ac76fc","resolution":{"observed_at":"2026-08-11T10:51:16.178177Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-10T22:20:58.810421Z","title":"Open graph benchmark: Datasets for machine learning on graphs","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2501.01951","last_updated":"2025-02-24T22:02:47Z","snapshot_observed_at":"2026-08-16T08:14:46.900493Z","submitted_at":"2025-01-03T18:54:46Z","title":"MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T22:20:58.810421Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2501.01951"},"observation_digest":"sha256:8cb11eb8770205e44e597aeaf42a49bcefbe75ba444767a48b545ea0680a21db","observation_id":"08c0e4d8-8ded-42a4-9b44-487f86315581","resolution":{"observed_at":"2026-08-10T22:20:58.810421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-10T14:28:16.785348Z","title":"Open graph benchmark: Datasets for machine learning on graphs","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2501.15348","last_updated":"2025-01-25T23:16:03Z","snapshot_observed_at":"2026-08-15T19:53:34.575722Z","submitted_at":"2025-01-25T23:16:03Z","title":"ReInc: Scaling Training of Dynamic Graph Neural Networks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T14:28:16.785348Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2501.15348"},"observation_digest":"sha256:f34485fb687736d90b5639ed58d3fc21e7c282c6175f646d837ac8fa4e18456e","observation_id":"940e5b22-fbb8-4a2e-89bb-b9ead694f30e","resolution":{"observed_at":"2026-08-10T14:28:16.785348Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-09T19:59:58.762819Z","title":"Open graph benchmark: Datasets for machine learning on graphs","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2502.00190","last_updated":"2025-01-31T22:13:24Z","snapshot_observed_at":"2026-08-16T19:17:09.213360Z","submitted_at":"2025-01-31T22:13:24Z","title":"On the Effectiveness of Random Weights in Graph Neural Networks","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-09T19:59:58.762819Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2502.00190"},"observation_digest":"sha256:81adabba3378b2ea479270d6cf7b4ba0a38055e61f210eb29bdbd6e2a00a68c4","observation_id":"3e925a93-dc90-49fd-904e-0cbe667515bb","resolution":{"observed_at":"2026-08-09T19:59:58.762819Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-08T20:29:47.178238Z","title":"Open graph benchmark: Datasets for machine learning on graphs,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2502.05063","last_updated":"2025-02-07T16:35:06Z","snapshot_observed_at":"2026-08-17T17:40:47.015933Z","submitted_at":"2025-02-07T16:35:06Z","title":"Computing and Learning on Combinatorial Data","version":1},"reference_index":259,"source":"pdf_text","source_observed_at":"2026-08-08T20:29:47.178238Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2502.05063"},"observation_digest":"sha256:7390351d6ad59044dcd130cf79dd50825d6b8a86b53fb007ba51d472ec8b6513","observation_id":"0f642535-ceba-486c-bf20-5abdb34707db","resolution":{"observed_at":"2026-08-08T20:29:47.178238Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-08T13:03:13.869632Z","title":"Open graph benchmark: Datasets for machine learning on graphs","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2502.07364","last_updated":"2025-05-29T01:54:10Z","snapshot_observed_at":"2026-08-18T09:34:56.232109Z","submitted_at":"2025-02-11T08:36:38Z","title":"Effects of Dropout on Performance in Long-range Graph Learning Tasks","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-08T13:03:13.869632Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2502.07364"},"observation_digest":"sha256:8722f53d62ffa61a7d6572522ef538ca6a085100753d163db025ee7483c81fb2","observation_id":"7296b421-3244-4327-b68a-16183b01ff05","resolution":{"observed_at":"2026-08-08T13:03:13.869632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-16T10:19:33.955710Z","title":"Open graph benchmark: Datasets for machine learning on graphs,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.18696","last_updated":"2025-04-25T21:05:28Z","snapshot_observed_at":"2026-08-18T09:31:38.270078Z","submitted_at":"2025-04-25T21:05:28Z","title":"Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T10:19:33.955710Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2504.18696"},"observation_digest":"sha256:f5109ba6e2a47a2b376fe42cd58448681d7c2da4d43b8db02afda17abc89b121","observation_id":"9896b0d5-edd6-49ca-ac09-393412c7c530","resolution":{"observed_at":"2026-08-16T10:19:33.955710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-16T01:08:47.809212Z","title":"Open graph benchmark: Datasets for machine learning on graphs, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.02124","last_updated":"2025-05-04T14:14:24Z","snapshot_observed_at":"2026-08-18T09:31:39.377506Z","submitted_at":"2025-05-04T14:14:24Z","title":"GRAIL: Graph Edit Distance and Node Alignment Using LLM-Generated Code","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-16T01:08:47.809212Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2505.02124"},"observation_digest":"sha256:57a928f277de531d3f8e2b7fbd65c6b24b5ac0cb9e62c18ecb9ddb9c2f4a92a9","observation_id":"6c97110a-06b5-41ec-ab7c-01f2d63578c2","resolution":{"observed_at":"2026-08-16T01:08:47.809212Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-15T22:07:36.723860Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.08098","last_updated":"2025-05-12T22:09:05Z","snapshot_observed_at":"2026-08-18T16:06:50.213312Z","submitted_at":"2025-05-12T22:09:05Z","title":"Fused3S: Fast Sparse Attention on Tensor Cores","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T22:07:36.723860Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2505.08098"},"observation_digest":"sha256:39eade56ca5be014c70f2a01cc49fd5e9fbb5cbb8a04ab314f00364a2111175e","observation_id":"81eb9813-e817-4c86-abb7-d4c08859773a","resolution":{"observed_at":"2026-08-15T22:07:36.723860Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-15T21:08:42.912181Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs, February 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.10711","last_updated":"2025-05-15T21:14:30Z","snapshot_observed_at":"2026-08-16T04:05:24.862045Z","submitted_at":"2025-05-15T21:14:30Z","title":"GNN-Suite: a Graph Neural Network Benchmarking Framework for Biomedical Informatics","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:42.912181Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2505.10711"},"observation_digest":"sha256:e593f166470faf204ab1d03c44d25ed270af9c08c1adf5daa93b14260ca37398","observation_id":"5c484b94-d834-4b66-8dfd-7c9cd761ad54","resolution":{"observed_at":"2026-08-15T21:08:42.912181Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-15T21:10:58.983096Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2506.00009","last_updated":"2025-05-15T19:50:11Z","snapshot_observed_at":"2026-08-17T12:19:48.852636Z","submitted_at":"2025-05-15T19:50:11Z","title":"MolTextNet: A Two-Million Molecule-Text Dataset for Multimodal Molecular Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T21:10:58.983096Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2506.00009"},"observation_digest":"sha256:4326f9aa347198a26e0cf27a95db8a66be47b78826aa1a5bbb22a9ff8b3ab060","observation_id":"6d9d9eb2-86c2-45d4-8794-ace4e1fc0e07","resolution":{"observed_at":"2026-08-15T21:10:58.983096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-07T11:34:52.971404Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.02134","last_updated":"2025-06-02T18:06:02Z","snapshot_observed_at":"2026-08-18T09:31:33.603613Z","submitted_at":"2025-06-02T18:06:02Z","title":"ReconXF: Graph Reconstruction Attack via Public Feature Explanations on Privatized Node Features and Labels","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T11:34:52.971404Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2506.02134"},"observation_digest":"sha256:5466fd1a458804419d65bebf7e6ab638faf64b084965f9a0c40eabc3a08f688b","observation_id":"08a8fcbe-ad34-41cc-9afd-ab6acb71cfa2","resolution":{"observed_at":"2026-08-07T11:34:52.971404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-07T10:43:58.034479Z","title":"Open graph benchmark: Datasets for machine learning on graphs","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2506.04653","last_updated":"2025-06-05T05:49:12Z","snapshot_observed_at":"2026-08-18T09:31:36.834403Z","submitted_at":"2025-06-05T05:49:12Z","title":"The Oversmoothing Fallacy: A Misguided Narrative in GNN Research","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T10:43:58.034479Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2506.04653"},"observation_digest":"sha256:7c856084176774445e9fbd03196d9d465f76bfe41a61ca423403c73445eeeed0","observation_id":"e5b61d6e-a1d7-4568-910c-10c06414de59","resolution":{"observed_at":"2026-08-07T10:43:58.034479Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-07T13:24:02.297442Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.10014","last_updated":"2025-05-28T04:48:43Z","snapshot_observed_at":"2026-08-18T03:52:28.738695Z","submitted_at":"2025-05-28T04:48:43Z","title":"NOCL: Node-Oriented Conceptualization LLM for Graph Tasks without Message Passing","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:02.297442Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2506.10014"},"observation_digest":"sha256:e79e310f707d77c3a07fe8ec9e26ab9345146ef2c0118d3243927e8eedaf0f2d","observation_id":"c9b9c978-8514-4096-ba95-63c0e5da51c6","resolution":{"observed_at":"2026-08-07T13:24:02.297442Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-15T20:00:57.858995Z","title":"Open graph benchmark: Datasets for machine learning on graphs","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2506.14098","last_updated":"2025-06-17T01:28:34Z","snapshot_observed_at":"2026-08-18T09:25:16.448741Z","submitted_at":"2025-06-17T01:28:34Z","title":"Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T20:00:57.858995Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2506.14098"},"observation_digest":"sha256:3198dbe0c67c25a8d6fec36f47be2a955217dc930cae307368d1bb82eee34280","observation_id":"3a1510c9-837c-4907-b226-b696858b67aa","resolution":{"observed_at":"2026-08-15T20:00:57.858995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-15T18:08:13.414524Z","title":"Open graph benchmark: Datasets for machine learning on graphs,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2507.19031","last_updated":"2025-07-25T07:35:09Z","snapshot_observed_at":"2026-08-18T09:31:35.177483Z","submitted_at":"2025-07-25T07:35:09Z","title":"ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T18:08:13.414524Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2507.19031"},"observation_digest":"sha256:1a4882b6d91ad2f16c8b2d3812118594ce02bb41110449eed461410c29521f3e","observation_id":"2af4eac9-4e06-417c-a00f-588a14b09972","resolution":{"observed_at":"2026-08-15T18:08:13.414524Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-05T18:43:44.238519Z","title":"Open graph benchmark: Datasets for machine learning on graphs, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.14352","last_updated":"2025-08-20T01:47:46Z","snapshot_observed_at":"2026-08-15T19:30:02.922687Z","submitted_at":"2025-08-20T01:47:46Z","title":"SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-05T18:43:44.238519Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2508.14352"},"observation_digest":"sha256:35565a1bcc8dc852e0e8a832190f9080cc0522dbbaa8b482031b7f61e27bf26d","observation_id":"3abc0a6f-d97e-4720-a462-38995d49bdec","resolution":{"observed_at":"2026-08-05T18:43:44.238519Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-05T15:53:52.465158Z","title":"Open graph benchmark: Datasets for machine learning on graphs","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2508.19352","last_updated":"2025-09-03T08:38:08Z","snapshot_observed_at":"2026-08-17T19:18:47.408074Z","submitted_at":"2025-08-26T18:25:54Z","title":"Memorization in Graph Neural Networks","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T15:53:52.465158Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2508.19352"},"observation_digest":"sha256:9831fd3ff4051ffea3471f51aa4599e813d6a24d258fc7a2d9c08d5f7fb99369","observation_id":"61baf7f2-f2ab-4736-b375-469c944253b7","resolution":{"observed_at":"2026-08-05T15:53:52.465158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-15T16:47:43.605247Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2508.20583","last_updated":"2025-08-28T09:20:47Z","snapshot_observed_at":"2026-08-16T07:19:00.440841Z","submitted_at":"2025-08-28T09:20:47Z","title":"A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-15T16:47:43.605247Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2508.20583"},"observation_digest":"sha256:f60498fcb191df9dfdc98b68fc5e4e83982ba5c6fa6f00d6fc97b60d98aad4fa","observation_id":"fc7841b2-f986-42a8-95b1-4837cde75461","resolution":{"observed_at":"2026-08-15T16:47:43.605247Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-05T13:23:08.533196Z","title":"ArXiv, abs/2005.00687","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2509.00735","last_updated":"2025-08-31T08:04:43Z","snapshot_observed_at":"2026-08-16T10:47:30.417201Z","submitted_at":"2025-08-31T08:04:43Z","title":"Task-Aware Adaptive Modulation: A Replay-Free and Resource-Efficient Approach For Continual Graph Learning","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-05T13:23:08.533196Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2509.00735"},"observation_digest":"sha256:af3e3d3016ee0128f1a63fd4fecea73c9758d0a1e64e3a7c9ae1088198c53be8","observation_id":"9d0a692f-520b-41d9-8258-dd55b59d57a0","resolution":{"observed_at":"2026-08-05T13:23:08.533196Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2512.20178","last_updated":"2026-05-13T06:42:42Z","snapshot_observed_at":"2026-08-15T20:30:13.033174Z","submitted_at":"2025-12-23T09:16:52Z","title":"SHIRO: Near-Optimal Communication Strategies for Distributed Sparse Matrix Multiplication","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-16T20:38:25.847537Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2512.20178"},"observation_digest":"sha256:e7e9e1b8d01166f0cdafb0bf08e71b7814bc8414b24b209ba8e89795988ab3f8","observation_id":"e39185db-53b3-4286-9663-a6ea95366768","resolution":{"observed_at":"2026-05-16T20:41:15.416943Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-03T05:44:24.825958Z","title":"Fey, M., Sunil, J., Nitta, A., Puri, R., Shah, M., Stojanoviˇc, B., Bendias, R., Barghi, A., Kocijan, V ., Zhang, Z., He, X., Lenssen, J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.01553","last_updated":"2026-07-26T09:03:31Z","snapshot_observed_at":"2026-08-13T21:06:21.235865Z","submitted_at":"2026-02-02T02:45:52Z","title":"Plain Transformers are Surprisingly Powerful Link Predictors","version":3},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-03T05:44:24.825958Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2602.01553"},"observation_digest":"sha256:eb193550be4786e162d0de0939e9073c440df914c093d561970080549f5bf3af","observation_id":"bbecfb7f-041c-48f9-a8a0-c34f121fe244","resolution":{"observed_at":"2026-08-03T05:44:24.825958Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-02T21:41:05.520058Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.19330","last_updated":"2026-06-07T16:19:55Z","snapshot_observed_at":"2026-08-16T10:48:14.415815Z","submitted_at":"2026-02-22T20:29:30Z","title":"CTS-Bench: Benchmarking Graph Coarsening Trade-offs for GNNs in Clock Tree Synthesis","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T21:41:05.520058Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2602.19330"},"observation_digest":"sha256:57956fe07b4b4a01584468f10e0db0b3ce4b2c0f26a12b13d2be855daf7afeca","observation_id":"e34aa49d-df5f-4a42-99e7-256370da68de","resolution":{"observed_at":"2026-08-02T21:41:05.520058Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2604.02651","last_updated":"2026-04-03T02:30:27Z","snapshot_observed_at":"2026-08-15T01:11:14.535875Z","submitted_at":"2026-04-03T02:30:27Z","title":"Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-13T20:38:31.444109Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2604.02651"},"observation_digest":"sha256:17e12e68cc5a705172691ba96d98fc5729923a9ba5ac0801e53d5999a71e40d5","observation_id":"7b95725f-008c-402c-af40-0d51c17cefe4","resolution":{"observed_at":"2026-05-13T20:43:15.341475Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2604.21093","last_updated":"2026-04-22T21:23:12Z","snapshot_observed_at":"2026-08-16T01:45:14.896417Z","submitted_at":"2026-04-22T21:23:12Z","title":"TRAVELFRAUDBENCH: A Configurable Evaluation Framework for GNN Fraud Ring Detection in Travel Networks","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-10T00:30:25.954515Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2604.21093"},"observation_digest":"sha256:d872f9912d401d2fd417bfa2a5a55e51ef5412dc893b5fdeaf1d9fbaa198076a","observation_id":"cb21a7f0-be7a-4660-ab8b-c357ef5cea3f","resolution":{"observed_at":"2026-05-11T13:51:02.425249Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2604.27985","last_updated":"2026-08-09T19:01:48Z","snapshot_observed_at":"2026-08-17T17:24:32.578019Z","submitted_at":"2026-04-30T15:14:30Z","title":"Exploring Sparse Matrix Multiplication Kernels on the Cerebras CS-3","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-07T06:14:12.828938Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2604.27985"},"observation_digest":"sha256:109f4fde5449b41327a0d707e5281fb35fa0f9d4453efaccdc5edc132b3853a6","observation_id":"c317fa1d-b5cc-47a9-a6f9-5abe9ae3cc81","resolution":{"observed_at":"2026-05-12T10:26:28.875922Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2605.01310","last_updated":"2026-05-02T07:54:52Z","snapshot_observed_at":"2026-08-03T06:51:13.707895Z","submitted_at":"2026-05-02T07:54:52Z","title":"GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-09T14:36:29.620205Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2605.01310"},"observation_digest":"sha256:1a23f9ba48e08ae92233840e4991b1d586430fc61e2594c92d0d6c06f07405f6","observation_id":"c090f555-c509-43e3-b91c-16ecb461ed56","resolution":{"observed_at":"2026-05-11T16:51:09.449829Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2605.01484","last_updated":"2026-05-02T15:11:52Z","snapshot_observed_at":"2026-08-02T04:58:06.359776Z","submitted_at":"2026-05-02T15:11:52Z","title":"Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks","version":1},"reference_index":204,"source":"arxiv_source","source_observed_at":"2026-05-09T15:09:03.417040Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2605.01484"},"observation_digest":"sha256:e14edf723d5379871bbc957373530e4e6e0be3800515070fe4037a374e37b12d","observation_id":"39d8451b-57d8-41a5-9ce2-6f22d192f1c9","resolution":{"observed_at":"2026-05-11T16:46:06.622014Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2605.02150","last_updated":"2026-05-04T02:22:43Z","snapshot_observed_at":"2026-08-12T15:14:05.534201Z","submitted_at":"2026-05-04T02:22:43Z","title":"H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-08T02:31:27.091956Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2605.02150"},"observation_digest":"sha256:abf80da1cc2fb56d05527368784879e0b450e9c1c609ab4413efa789237b8948","observation_id":"8117d752-b3ab-46d3-88f8-b522204428d1","resolution":{"observed_at":"2026-05-11T22:46:11.712695Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2605.19916","last_updated":"2026-05-19T14:40:51Z","snapshot_observed_at":"2026-08-15T13:29:55.108772Z","submitted_at":"2026-05-19T14:40:51Z","title":"Fast and Featureless Node Representation Learning with Partial Pairwise Supervision","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-20T06:39:02.277629Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2605.19916"},"observation_digest":"sha256:ba89776bfc9cc7fbe2355daf4f725bc15642355212874d6731fc2b167730ea3a","observation_id":"c6c86121-e5ef-4ef3-84b2-6b53ee280dcd","resolution":{"observed_at":"2026-05-20T06:43:06.025290Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2605.22385","last_updated":"2026-05-21T12:16:19Z","snapshot_observed_at":"2026-07-06T23:32:44.699825Z","submitted_at":"2026-05-21T12:16:19Z","title":"Efficient Higher-order Subgraph Attribution via Message Passing","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-05-22T07:53:40.841765Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2605.22385"},"observation_digest":"sha256:6d8c47bbb5d8c55bdc8a8c845628df7aac3e5ee829f77f9911b7b752d6f041e7","observation_id":"4e0b7c82-a3f9-4318-89d1-4fd4d284f189","resolution":{"observed_at":"2026-05-22T07:54:43.110168Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2605.23673","last_updated":"2026-05-22T14:21:37Z","snapshot_observed_at":"2026-08-19T06:56:50.437925Z","submitted_at":"2026-05-22T14:21:37Z","title":"Relevant Walk Search for Explaining Graph Neural Networks","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-05-25T05:12:00.156450Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2605.23673"},"observation_digest":"sha256:45db5b45db5735a2ce600573789e1568755330e6c177b5ec3ad86ccb1b660200","observation_id":"c9c1e942-d2c1-4a2d-b8bd-be469393b86d","resolution":{"observed_at":"2026-05-25T05:15:22.514791Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2605.23708","last_updated":"2026-05-22T14:55:09Z","snapshot_observed_at":"2026-08-18T14:10:43.798815Z","submitted_at":"2026-05-22T14:55:09Z","title":"Learning Dynamic Stability Landscapes in Synchronization Networks","version":1},"reference_index":295,"source":"arxiv_source","source_observed_at":"2026-05-25T05:04:16.957305Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2605.23708"},"observation_digest":"sha256:864b912ad5c61a1c4edcd3c75a55736480f37ac4b53ad8216ffad8231824cad2","observation_id":"f9ee4208-18a2-4309-8809-9536a09232d8","resolution":{"observed_at":"2026-05-25T05:05:22.674246Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2605.31500","last_updated":"2026-05-29T16:22:45Z","snapshot_observed_at":"2026-08-14T23:52:33.085463Z","submitted_at":"2026-05-29T16:22:45Z","title":"On Efficient Scaling of GNNs via IO-Aware Layers Implementations","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-28T22:52:59.081816Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2605.31500"},"observation_digest":"sha256:bb0c724f8b5f2e63367ae629448439f52d58fe212f644ae532852fea4828e0e0","observation_id":"cf414894-db55-49a4-8cab-25bfdf5e2971","resolution":{"observed_at":"2026-07-01T19:16:01.092980Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2606.17667","last_updated":"2026-06-16T08:29:34Z","snapshot_observed_at":"2026-08-16T12:45:24.831739Z","submitted_at":"2026-06-16T08:29:34Z","title":"Handling Feature Heterogeneity with Learnable Graph Patches","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-27T01:36:45.977332Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2606.17667"},"observation_digest":"sha256:d8f3a597d544b7e43c4825afac7fdc4c80a975b99bcfe886f7b4c2a90435ff7e","observation_id":"2cb18971-1bb4-4bb7-9220-4955a45b0329","resolution":{"observed_at":"2026-07-03T20:08:56.272946Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2606.19501","last_updated":"2026-06-29T18:36:47Z","snapshot_observed_at":"2026-08-15T15:21:16.322129Z","submitted_at":"2026-06-17T18:40:08Z","title":"DeXposure-Claw: An Agentic System for DeFi Risk Supervision","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-06-26T20:57:32.647600Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2606.19501"},"observation_digest":"sha256:3e8a0ca09f28c2bb654a26ddfadf56f8d9f7dbdbe7fa0f38f795212b6b419d6c","observation_id":"635dfa1c-7879-40b3-be02-91831e9c633d","resolution":{"observed_at":"2026-07-04T00:49:18.650500Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2606.19501","last_updated":"2026-06-29T18:36:47Z","snapshot_observed_at":"2026-08-15T15:21:16.322129Z","submitted_at":"2026-06-17T18:40:08Z","title":"DeXposure-Claw: An Agentic System for DeFi Risk Supervision","version":2},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-07-01T07:31:47.518981Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2606.19501"},"observation_digest":"sha256:7b0241ed11080696ff203249c4650a6aba7d10d70dddfcea0f3433af98d14eef","observation_id":"777c6378-e6a1-4b10-be41-53ac21aeaa8a","resolution":{"observed_at":"2026-07-01T07:35:28.916229Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2606.27917","last_updated":"2026-06-26T10:07:10Z","snapshot_observed_at":"2026-08-13T13:58:21.044857Z","submitted_at":"2026-06-26T10:07:10Z","title":"Graph Dimensionality Reduction for Contextual Bandits: Structure-Specific Regret Bounds under Approximate Smoothness and Noisy Eigenspaces","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T04:24:32.483334Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2606.27917"},"observation_digest":"sha256:81996cf3673d2a97268a971a0ea5bf2da5e3451c5436ca28e6ffd31ba83ebe13","observation_id":"03b7b94a-8448-4059-b73d-989f97d78aca","resolution":{"observed_at":"2026-07-01T16:55:51.224765Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":"2005.00687","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-04T00:49:18.647891Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":"febc0dde-22e6-4f9c-b652-10e1c530b2cc","year":2005},"citing_paper":{"arxiv_id":"2606.30011","last_updated":"2026-06-29T09:18:11Z","snapshot_observed_at":"2026-08-13T12:18:42.225918Z","submitted_at":"2026-06-29T09:18:11Z","title":"T3R: Deeper Test-Time Adaptation for Graph Neural Networks via Gradient Rotation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-30T07:26:05.735508Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2606.30011"},"observation_digest":"sha256:9a5c843aa6dab716bea5cbea0e7e84eda73c1bdb5e3470ca9ca8a90023273ccb","observation_id":"8a99fc3c-e001-4dd7-af92-1012b2789030","resolution":{"observed_at":"2026-06-30T07:34:21.948157Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-01T13:29:53.256490Z","title":"Advances in Neural Information Processing Systems , year=","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2607.19108","last_updated":"2026-07-21T13:50:24Z","snapshot_observed_at":"2026-08-18T09:30:56.984131Z","submitted_at":"2026-07-21T13:50:24Z","title":"OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-01T13:29:53.256490Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2607.19108"},"observation_digest":"sha256:dd64193cd027ddc0b787bbf8cb9b211b837a7625b3d79841cc7b5cafff48ace2","observation_id":"a8e80baa-2eae-4e1d-b89d-11e338f23aa8","resolution":{"observed_at":"2026-08-01T13:29:53.256490Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-01T13:26:33.058578Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2607.19128","last_updated":"2026-07-21T14:18:25Z","snapshot_observed_at":"2026-08-18T20:32:57.286588Z","submitted_at":"2026-07-21T14:18:25Z","title":"One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-01T13:26:33.058578Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2607.19128"},"observation_digest":"sha256:bd5f854e5cee72d2aae85257bdb8b71515ecf13d0e9c671d05937984a55214a6","observation_id":"d0ea85ae-5b2f-45b1-b1d9-fe8540d38f3b","resolution":{"observed_at":"2026-08-01T13:26:33.058578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-01T00:04:26.891576Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.23225","last_updated":"2026-07-25T14:23:27Z","snapshot_observed_at":"2026-08-13T10:35:57.283171Z","submitted_at":"2026-07-25T14:23:27Z","title":"ParasGB: A Graph Benchmark Suite for Parasitic Estimation on AMS Circuits","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T00:04:26.891576Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2607.23225"},"observation_digest":"sha256:e5b058bac6dded846e10d193e4d4a44eef8a93a1028527ef4b03c4bce104c0de","observation_id":"5c86a81a-e22a-4a05-9b72-a1c0f9866722","resolution":{"observed_at":"2026-08-01T00:04:26.891576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-07-31T18:33:46.767206Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2607.24314","last_updated":"2026-07-27T11:58:56Z","snapshot_observed_at":"2026-08-17T19:37:06.766053Z","submitted_at":"2026-07-27T11:58:56Z","title":"MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-31T18:33:46.767206Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2607.24314"},"observation_digest":"sha256:0b2612175aa602c79e4ad0ac14685d7f5202ecbe28a9137be07aaf43d5a32d95","observation_id":"9513f994-a1ae-471c-bfd8-1a8f39f75927","resolution":{"observed_at":"2026-07-31T18:33:46.767206Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-01T09:58:38.391913Z","title":"arXiv preprint arXiv:2005.00687 (2020), https://arxiv.org/abs/2005.00687","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2607.27303","last_updated":"2026-07-29T17:04:30Z","snapshot_observed_at":"2026-08-16T17:22:45.214093Z","submitted_at":"2026-07-29T17:04:30Z","title":"THGFM: Dual-Branch Temporal Heterogeneous Graph Fusion Model","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T09:58:38.391913Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2607.27303"},"observation_digest":"sha256:90ed7408a4e161c47e4ada4afbc8be4f953ca4861a507f6735ef07de4e457dce","observation_id":"2373ea57-5968-4296-b834-8c67b20d19d7","resolution":{"observed_at":"2026-08-01T09:58:38.391913Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-07T20:00:45.962410Z","title":"Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and J","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.05982","last_updated":"2026-08-06T12:59:05Z","snapshot_observed_at":"2026-08-16T23:15:41.146337Z","submitted_at":"2026-08-06T12:59:05Z","title":"THBKG: A Temporal Biomedical Knowledge Graph for Decision-Aligned Clinical Advancement Prediction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T20:00:45.962410Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2608.05982"},"observation_digest":"sha256:7d9d3ff75069acb37c44637dc6d23ca46cdfaf41a73fa261421ae51547f3bd5b","observation_id":"b54ff34f-0946-423c-992a-f26465db395f","resolution":{"observed_at":"2026-08-07T20:00:45.962410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-10T04:32:43.144169Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2608.06430","last_updated":"2026-08-05T23:27:40Z","snapshot_observed_at":"2026-08-18T22:56:07.044640Z","submitted_at":"2026-08-05T23:27:40Z","title":"MiGHT-EHR: A Multi-task Graph Transformer for Heterogeneous Temporal Electronic Health Records","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-10T04:32:43.144169Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2608.06430"},"observation_digest":"sha256:fdb050a644d49ae6945f9e1d275318a5bfaf3462301aee288d8fab33c1abd82c","observation_id":"e50552a8-7fb1-4d63-8e36-34d76fe9582e","resolution":{"observed_at":"2026-08-10T04:32:43.144169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-10T04:30:57.364939Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2608.06441","last_updated":"2026-08-06T11:03:36Z","snapshot_observed_at":"2026-08-19T13:40:06.618662Z","submitted_at":"2026-08-06T11:03:36Z","title":"SNI-GNN: SmartNIC-Assisted Full-Graph GNN Training with In-Network Embedding Prediction","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:57.364939Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2608.06441"},"observation_digest":"sha256:6ba233336a56a61c0c0518883a6c1338aa9da19ebd586c5d86f3daf14a8b554c","observation_id":"7afafab2-8717-4c02-b136-b54fbed61bf7","resolution":{"observed_at":"2026-08-10T04:30:57.364939Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-11T00:24:53.863601Z","title":"Open graph benchmark: Datasets for machine learning on graphs,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2608.07733","last_updated":"2026-08-07T19:55:45Z","snapshot_observed_at":"2026-08-17T08:04:24.402148Z","submitted_at":"2026-08-07T19:55:45Z","title":"LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T00:24:53.863601Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2608.07733"},"observation_digest":"sha256:d54d6ff81bd1c6dc4eaa4bd09f2f654d9a10565a8472f38eadeb5ff17b1c91ec","observation_id":"e501fdc0-2e80-483f-b4d5-5065e60f52e2","resolution":{"observed_at":"2026-08-11T00:24:53.863601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00687","snapshot_observed_at":"2026-08-14T04:38:54.283876Z","title":"arXiv preprint arXiv:2005.00687 , year=","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2608.08567","last_updated":"2026-08-13T16:24:30Z","snapshot_observed_at":"2026-08-18T12:57:25.464825Z","submitted_at":"2026-08-09T08:14:35Z","title":"Neural Message Passing on Structural Interaction Graphs for Fully-Inductive Graph Neural Networks","version":2},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-14T04:38:54.283876Z"},"links":{"cited_paper":"/paper/2005.00687","citing_paper":"/paper/2608.08567"},"observation_digest":"sha256:c7914051e9d767c8b3ba34ac5d48cdabae434bdc83ad47219c419b88db25ee9a","observation_id":"2740b79f-1747-479e-816c-3182420fc23a","resolution":{"observed_at":"2026-08-14T04:38:54.283876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2005.00687/citation-record","integrity":"/paper/2005.00687/integrity","json":"/paper/2005.00687/citation-record.json","paper":"/paper/2005.00687"},"outbound":[],"paper":{"arxiv_id":"2005.00687","last_updated":"2021-02-25T02:06:27Z","latest_version":7,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T09:31:00.189576Z","submitted_at":"2020-05-02T03:09:50Z","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs"},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 59 inbound Pith citation observations for arXiv:2005.00687."}