{"as_of":"2026-08-07T09:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d8d2f40b57584cdfb2627976a97fdc5230f96545ab77ae914a02077eabb2b456","coverage":[{"denominator":79,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":79,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T18:41:21.823852Z","state":"measured"},{"denominator":79,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":79,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2508.14338/citation-record","integrity":"/paper/2508.14338/integrity","json":"/paper/2508.14338/citation-record.json","paper":"/paper/2508.14338"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:34.992136Z","title":"A convergence analysis of gradient descent on graph neural networks","venue":null,"work_id":"25492f2c-01ba-4133-9b6c-52b2c1e3ca18","year":2021},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:14.484549Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:ba6d571b759ec2b280ed7d55bb1c20abb0737b329d5174be1f6c0eb0c26a337a","observation_id":"877bfa7f-2429-488b-ab34-021d5341b98e","resolution":{"observed_at":"2026-08-05T18:41:35.104684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:34.659830Z","title":"and Moulines, E","venue":null,"work_id":"b5de8a22-311d-43bd-93bd-c3f305851c1d","year":2013},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:14.580166Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:836bf0b01aeafbeae2d5afa23721277df1033a344e5fb495d28eaf6b1a9a2a6b","observation_id":"a385dfdf-efd8-4ffc-8ad8-6b1d615d9b14","resolution":{"observed_at":"2026-08-05T18:41:34.846326Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.06966","last_updated":"2022-02-04T06:46:58Z","snapshot_observed_at":"2026-07-06T10:41:03.588448Z","submitted_at":"2021-02-13T17:46:57Z","title":"Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.06966","snapshot_observed_at":"2026-08-05T18:41:14.715907Z","title":"Graph convolution for semi-supervised classification: Improved linear separability and out-of-distribution generalization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:14.715907Z"},"links":{"cited_paper":"/paper/2102.06966","citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:e78148f190d07769cf60bb55bb032c8e208ec9ddc9a8d96fe102ad5a8ed470fe","observation_id":"1230ade7-1b76-48ac-be07-9e5727a48f9f","resolution":{"observed_at":"2026-08-05T18:41:14.715907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:34.358075Z","title":"L., Long, P","venue":null,"work_id":"80235076-b7a3-4e4c-8cfe-df16c5165c07","year":2020},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:14.876894Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:7773b859926bb72bf451431197974c6172ed622ba4ba6de96a0916b534f97772","observation_id":"fad8f795-04a9-402b-aadc-3782839576f7","resolution":{"observed_at":"2026-08-05T18:41:34.493383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:34.035056Z","title":"Tight nonparametric convergence rates for stochastic gradient descent under the noiseless linear model","venue":null,"work_id":"e8a61169-27e8-45d7-8c48-3174b8dce136","year":2020},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:15.032112Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:5137163e740aae92b3e116c27d9df247f9c32aef46c0e207c5eb52bbf32fd5ac","observation_id":"9ebb4865-9041-48f6-94fe-d1fb26f3b1cb","resolution":{"observed_at":"2026-08-05T18:41:34.175493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10568","last_updated":"2018-03-01T15:23:22Z","snapshot_observed_at":"2026-08-07T03:56:43.648704Z","submitted_at":"2017-10-29T06:14:00Z","title":"Stochastic Training of Graph Convolutional Networks with Variance Reduction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10568","snapshot_observed_at":"2026-08-05T18:41:15.153047Z","title":"Stochastic training of graph convolutional networks with variance reduction","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:15.153047Z"},"links":{"cited_paper":"/paper/1710.10568","citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:86f8da1cf34a9688581cdb9153ed60d234e1d58b5c2f5d3862ec358d7bad0c5d","observation_id":"04a591c0-3f7c-46f5-a2d2-17de600c2895","resolution":{"observed_at":"2026-08-05T18:41:15.153047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.10247","last_updated":"2018-01-30T22:36:16Z","snapshot_observed_at":"2026-07-06T06:21:01.297755Z","submitted_at":"2018-01-30T22:36:16Z","title":"FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.10247","snapshot_observed_at":"2026-08-05T18:41:15.327172Z","title":"Fastgcn: fast learning with graph convolutional networks via importance sampling","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:15.327172Z"},"links":{"cited_paper":"/paper/1801.10247","citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:869570cb090b8cabf613db07c221d218d29f970c93386c14afc92ceb68eb9052","observation_id":"185c7349-b403-46bb-9a24-792aa7a59f05","resolution":{"observed_at":"2026-08-05T18:41:15.327172Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:33.758086Z","title":"Eigenvalues of random power law graphs","venue":null,"work_id":"17c00011-7ae2-4f73-931b-45d3f011c540","year":2003},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:15.497079Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:c839bcdf3f2761d59facc39b19074c7cf35d30a7b3764dcb8b3695f30192d214","observation_id":"8b4a27b4-4404-4ef2-85c4-c78c9f682df3","resolution":{"observed_at":"2026-08-05T18:41:33.881089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:33.459512Z","title":null,"venue":null,"work_id":"c988c1c4-221c-42ae-896e-0f002582196c","year":1997},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:15.653112Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:2c7d8e31b46379241d6f0235d541b173a3454b3a7896ef93bba596ab62bdeb30","observation_id":"66b655b4-dad6-4cdd-b998-4561a2d3022a","resolution":{"observed_at":"2026-08-05T18:41:33.615845Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:33.213478Z","title":"and Bach, F","venue":null,"work_id":"2684c882-5bd3-494a-b37f-0381bfab1f09","year":2015},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:15.818499Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:9f9c964811c7f003d6983b73ab4676e0f746b52c00cc6d1a3de97356f6d57ac6","observation_id":"fab6d47b-5c5a-4623-8570-0f747887d238","resolution":{"observed_at":"2026-08-05T18:41:33.337574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:33.145169Z","title":"S., Foster, D","venue":null,"work_id":"a06bd9ce-6596-4e61-a5d4-544bb4def264","year":2013},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:15.944496Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:03e635cccd004d4fb02d73477d19330f4bcce4ecc72b02155e1b5bc274c400f1","observation_id":"ec312054-a9b4-4af7-a8e4-abbbec563376","resolution":{"observed_at":"2026-08-05T18:41:33.201762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:32.930501Z","title":"Harder, better, faster, stronger convergence rates for least-squares regression","venue":null,"work_id":"e8286ffc-1f04-4154-97b5-c93d8ba22a27","year":2017},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:16.076181Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:5459fac23aad15497f0979d2ba7383765a31148482560096444612d6e1098740","observation_id":"427fd1b6-e7d5-4125-bbec-91691c6f32a3","resolution":{"observed_at":"2026-08-05T18:41:32.996006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:32.742945Z","title":"and Wager, S","venue":null,"work_id":"7e14b67a-9254-4817-847f-0c2c32c51ce1","year":2018},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:16.165240Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:0ecd43930b783fb141e2bd9f609608143d732e0f3dd6b68c99d8e7ee90117ca1","observation_id":"3767df2a-7e04-41c8-bb0b-165511b99872","resolution":{"observed_at":"2026-08-05T18:41:32.847541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:32.626819Z","title":"S., Hou, K., Salakhutdinov, R","venue":null,"work_id":"f8d64fde-d811-4747-a9e3-93c6a64f1fb6","year":2019},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:16.275227Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:259ff66ecd31082f482e820ae108d48225868d66a5b672812a5b0068ee8900d4","observation_id":"c0410d9e-691a-4225-a9c2-0bdca5a659bf","resolution":{"observed_at":"2026-08-05T18:41:32.678288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:32.500253Z","title":"Networks, crowds, and markets, volume 8","venue":null,"work_id":"32bb18af-51e9-4954-9ab5-682bdd6cf250","year":2010},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:16.401466Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:0cea1304f7ad79a1239ad4e457cc8783ddaf94b746cfecc02223e832e02b05c4","observation_id":"8d2d2d94-0ab9-4e5d-92d9-ba027b1ceec4","resolution":{"observed_at":"2026-08-05T18:41:32.556885Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:32.320431Z","title":"On power-law relationships of the internet topology","venue":null,"work_id":"b6e99270-437f-4e2f-9a68-d5be8ae58e0d","year":1999},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:16.499557Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:18285a3cd6dbe9f850cb7450083916571d1c238f74888944d6874072582bb705","observation_id":"7d28f397-d68c-4c7b-893c-cfad77703a47","resolution":{"observed_at":"2026-08-05T18:41:32.411426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:32.137711Z","title":"real-world","venue":null,"work_id":"cf1d4b74-a052-4b51-886d-eb7048f732e9","year":2001},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:16.607280Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:8a88b412de78ab69a7d15501c199ef64c674daa7151dc51ade6deac47f5f447e","observation_id":"993218e3-1908-4187-b057-40f7c8665233","resolution":{"observed_at":"2026-08-05T18:41:32.230214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:31.938959Z","title":"Community detection in graphs","venue":null,"work_id":"6dfd24e2-b866-40f3-98a8-3f96fa3cbe63","year":2010},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:16.725554Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:dec671b479b35e14910c142bcf48b6b068865b928754ee3800ffbb5614a233bd","observation_id":"519f79b1-154e-4030-ba11-a149788e31f6","resolution":{"observed_at":"2026-08-05T18:41:32.024617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:31.743228Z","title":"Identifying network structure similarity using spectral graph theory","venue":null,"work_id":"37ee6482-180d-4a8b-abb8-30e90171a196","year":2018},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:16.813231Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:1c62ac3112a62e38dffed6e84e6b3e348802e44fc121bb73f58b2d163285fc0d","observation_id":"7496bb54-4448-4540-b661-87ec1596dc5b","resolution":{"observed_at":"2026-08-05T18:41:31.823040Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:31.544558Z","title":"S., Riley, P","venue":null,"work_id":"56232449-0e83-4992-b296-a92ccb0e3a49","year":2017},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:16.928402Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:7fc58d9ba6ecd86dd763accbd88582a195995c08a1ea8d6234620a6aa84e63d3","observation_id":"1b9c71ff-4436-4677-94ad-5193bf4ec67d","resolution":{"observed_at":"2026-08-05T18:41:31.634581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:31.364356Z","title":"Spectra and eigenvectors of scale-free networks","venue":null,"work_id":"db80eefc-4215-44d3-a872-0cbac9f4fe83","year":2001},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:17.002739Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:5aaf4d2c76a630948223cf146d9faa8e27ac1af856ad7ca9880dc2bfa478d28b","observation_id":"00dc4c8f-6477-4824-8c8e-d21c23bb749d","resolution":{"observed_at":"2026-08-05T18:41:31.442505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:31.140725Z","title":"Exploring network structure, dynamics, and function using networkx","venue":null,"work_id":"89c5de93-d54a-47d3-bbd2-f6c75ddd536e","year":2008},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:17.084936Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:3013f30a3e2e22de654bc25f07c9e45703bc54279098c120867bae50ba5e744f","observation_id":"643d31ff-8347-45fc-8115-ae545a20136e","resolution":{"observed_at":"2026-08-05T18:41:31.229132Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:30.992961Z","title":"L., Ying, R., and Leskovec, J","venue":null,"work_id":"8c0c8570-9848-40f1-86a9-0141cd8ecc82","year":2018},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:17.158408Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:00739f201f7292f4942ea7a0d21e6668061c49919448314c14e47a5f8aa58689","observation_id":"8c3d9452-e8d4-44a4-b99e-04016d02f01f","resolution":{"observed_at":"2026-08-05T18:41:31.069487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:30.817087Z","title":"K., Vandergheynst, P., and Gribonval, R","venue":null,"work_id":"51405c96-44ff-45ce-8a4a-9050d72cc7e2","year":2011},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:17.223725Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:93e4020d1b3b7adff909e5ff13660ae9deaf6bbb10e144a106bb91d2f38ba7ed","observation_id":"4f14e84d-4727-4ff2-8818-371c395cb739","resolution":{"observed_at":"2026-08-05T18:41:30.902648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:30.636051Z","title":"H., and Friedman, J","venue":null,"work_id":"0b2e67cf-eb8c-4399-8d89-3ac069629a09","year":2009},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:17.293573Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:124289f573cbe8a59adfe7df06e1256207cfcee4bfd53d2ecaaef4ea278175d7","observation_id":"d17f3d1c-c098-4a89-8f06-f7db69f3b11a","resolution":{"observed_at":"2026-08-05T18:41:30.715641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:30.467318Z","title":null,"venue":null,"work_id":"b4d3d6a2-759a-4261-a0df-aafcd46ad107","year":2022},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:17.367791Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:f0a1a3c17162a54f99efa7be9a6d508d8380f47c874ad43694864a3ab1cd3971","observation_id":"a57af952-617a-46dd-87e8-f31f28558c09","resolution":{"observed_at":"2026-08-05T18:41:30.520034Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:30.311021Z","title":"M., and Zhang, T","venue":null,"work_id":"b74b092f-d041-4c14-88b9-b0de78283d07","year":2012},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:17.461093Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:44dd6e7068b611039c6ad06375d1cb3150de98a53a688f4fd2fc76a344dd044e","observation_id":"edfeec16-751e-45ed-9b98-f5fb2230a08f","resolution":{"observed_at":"2026-08-05T18:41:30.368430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1809.05343","last_updated":"2018-11-19T07:50:26Z","snapshot_observed_at":"2026-07-06T07:02:12.971488Z","submitted_at":"2018-09-14T10:33:27Z","title":"Adaptive Sampling Towards Fast Graph Representation Learning","version":3},"cited_work":{"arxiv_id":"1809.05343","doi":null,"metadata_source":"pith","pith_arxiv_id":"1809.05343","snapshot_observed_at":"2026-08-05T18:41:22.700431Z","title":"Adaptive Sampling Towards Fast Graph Representation Learning","venue":"cs.CV","work_id":"568ea2ee-41bb-429b-be06-c267213c432e","year":2018},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:17.527494Z"},"links":{"cited_paper":"/paper/1809.05343","citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:0a5b578409b31cf696599002d2296b0695e8c0cff19a0fde0f83cff7e6563550","observation_id":"8940592f-9460-454e-ae3a-ce02a1ae50fa","resolution":{"observed_at":"2026-08-05T18:41:22.753927Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.09430","last_updated":"2018-07-21T21:13:33Z","snapshot_observed_at":"2026-07-06T06:06:06.859267Z","submitted_at":"2017-10-25T19:28:13Z","title":"A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares)","version":2},"cited_work":{"arxiv_id":"1710.09430","doi":null,"metadata_source":"pith","pith_arxiv_id":"1710.09430","snapshot_observed_at":"2026-08-05T18:41:22.504093Z","title":"A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares)","venue":"stat.ML","work_id":"4e06af59-8e74-48b4-b5f4-65b44361dadc","year":2017},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:17.589351Z"},"links":{"cited_paper":"/paper/1710.09430","citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:efe2549ca34923b97a973b613c106e9c5852c8dedd31728c5b68c8008710e72b","observation_id":"adedd71d-8d4a-4fc1-af59-3aca0247fecb","resolution":{"observed_at":"2026-08-05T18:41:22.593576Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:30.128531Z","title":"M., Kidambi, R., Netrapalli, P., and Sidford, A","venue":null,"work_id":"c4daec72-31e8-4e92-b076-21827b36a139","year":2018},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:17.657968Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:bba60b56dbcf219537dd6233657f60574a2e888c33e6641798cd90f418aed0f5","observation_id":"503d9771-b9ff-43f0-a68b-f4195f6b1a76","resolution":{"observed_at":"2026-08-05T18:41:30.206548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:29.919028Z","title":"Theory of graph neural networks: Representation and learning","venue":null,"work_id":"207ef7c0-87f9-45f4-a539-81b1174dd684","year":2022},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:17.742469Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:1b3896e3f5d6c9356e01f7d27aa624fb14e5eb0b3c49d8fa0546ab5f040fab1c","observation_id":"ac87c034-0f43-4ffa-8926-91fd71a43115","resolution":{"observed_at":"2026-08-05T18:41:30.010846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:29.741258Z","title":null,"venue":null,"work_id":"d4fccb70-b39a-4b69-bb0c-ac25d3bf3d3b","year":2017},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:17.857900Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:76d1bc450aac36e39fd4f49cdbb6c299d596ec858cbc1f86e39856c486606eb2","observation_id":"bb87baf8-05de-4594-8cf8-520e123c44e8","resolution":{"observed_at":"2026-08-05T18:41:29.824867Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:29.543768Z","title":"The optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization","venue":null,"work_id":"1e909408-75bf-4071-afa9-67e75e2bcc16","year":2020},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:17.959454Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:bd16adffc730008a8009820109198d3dacae50a8ac871a153360fdc2495318e6","observation_id":"857e8ffe-e44e-46da-924b-44d7aed32880","resolution":{"observed_at":"2026-08-05T18:41:29.622755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:29.347112Z","title":"and Szepesvari, C","venue":null,"work_id":"a6b0a6df-de15-47d4-b897-abe4274cc5e4","year":2018},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:18.044112Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:a033375cfe6d9f824ed4eafc10309d239ce6f24c097e5e1cca2d498a823d9b9c","observation_id":"69784b83-f544-4ab7-adff-ee2cdecf9ce5","resolution":{"observed_at":"2026-08-05T18:41:29.428686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:29.150171Z","title":"and Weisfeiler, B","venue":null,"work_id":"1f4a6430-9578-4823-80d2-a85da8a435d8","year":1968},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:18.129833Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:6fb5029865fad537347acc012ed1618087b4278a74b91ad16f3815b0ddcbef3a","observation_id":"a3e4ed05-3cfe-46b8-a0a3-db283bfec3cd","resolution":{"observed_at":"2026-08-05T18:41:29.261948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:18.200964Z","title":"Deeper insights into graph convolutional networks for semi-supervised learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:18.200964Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:5e104ba5c7f47e2331bc56ee23a30703ed44ecb6e37d10e61bd3ebb1877c9cab","observation_id":"ed4de45c-0723-47b6-9aca-89243fb68507","resolution":{"observed_at":"2026-08-05T18:41:18.200964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.01926","last_updated":"2018-02-22T19:52:51Z","snapshot_observed_at":"2026-08-01T21:20:06.448984Z","submitted_at":"2017-07-06T18:20:59Z","title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.01926","snapshot_observed_at":"2026-08-05T18:41:18.284884Z","title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:18.284884Z"},"links":{"cited_paper":"/paper/1707.01926","citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:f0d86eb497fadd0f68fcd30ecf9ead411751de672909da85498567e03783eb6a","observation_id":"94f5241d-fc8e-43d5-80a8-438de80f4c08","resolution":{"observed_at":"2026-08-05T18:41:18.284884Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:28.969575Z","title":"A \\ pac \\ -bayesian approach to generalization bounds for graph neural networks","venue":null,"work_id":"33293c6e-2236-4d97-a671-48ce3ca000b1","year":2021},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:18.396213Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:b6609c2faf638cd67004b69ffbdd1956edee45c452ea6a9689bda68f76b64b33","observation_id":"4f85d4ec-24c3-45a8-aeec-871273e32d08","resolution":{"observed_at":"2026-08-05T18:41:29.036900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:28.808935Z","title":"Visual relationship detection with language priors, 2016","venue":null,"work_id":"1bd8681d-5ebb-4946-9868-949e3d390eb7","year":2016},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:18.473710Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:bb10ca7c0fab86c78a444e8295bf440cf919c02bd4f967861521ea41e108fbca","observation_id":"1f900806-9668-417d-8dfb-33350f12b598","resolution":{"observed_at":"2026-08-05T18:41:28.884647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:28.596468Z","title":"Generalization bounds for graph convolutional neural networks via rademacher complexity, 2021","venue":null,"work_id":"86d13170-345b-488c-aa4a-798a5281a7fc","year":2021},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:18.620071Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:74c1d845ab6d8e160ae1ff406247a51d7aab0f2e4a276921c2268da14e3000fd","observation_id":"e863b2b0-6751-4766-9df7-66d05d5e0930","resolution":{"observed_at":"2026-08-05T18:41:28.697034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.15535","last_updated":"2021-11-30T18:02:14Z","snapshot_observed_at":"2026-07-06T11:24:16.418266Z","submitted_at":"2021-06-29T16:13:41Z","title":"Subgroup Generalization and Fairness of Graph Neural Networks","version":2},"cited_work":{"arxiv_id":"2106.15535","doi":null,"metadata_source":"pith","pith_arxiv_id":"2106.15535","snapshot_observed_at":"2026-08-05T18:41:22.287356Z","title":"Subgroup Generalization and Fairness of Graph Neural Networks","venue":"cs.LG","work_id":"07d6b92b-e3d1-4dde-ac05-b92a70d9c1f2","year":2021},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:18.717356Z"},"links":{"cited_paper":"/paper/2106.15535","citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:6478f148b76200ccf0d5f30b340a9e03525d90a8fd079414fe9aadfff6d79d71","observation_id":"af851030-7830-4616-a97e-535d040e0913","resolution":{"observed_at":"2026-08-05T18:41:22.380704Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:28.419868Z","title":"and Suzuki, T","venue":null,"work_id":"32ac3162-f620-4753-8fd7-c11ad400db61","year":2020},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:18.811017Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:f6a9a98a387709a354289bd82146d2507473a152874154de054ff4d3b4c69bea","observation_id":"bbbbf1b5-ad54-4d81-8e08-b3454faff887","resolution":{"observed_at":"2026-08-05T18:41:28.491284Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:28.265093Z","title":"Implicit regularization or implicit conditioning? exact risk trajectories of sgd in high dimensions","venue":null,"work_id":"9ba51b9b-84f5-4717-868b-029645b7eb15","year":2022},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:18.913132Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:8c3983a052eaef5a32140281ad50e548ecec5a8a3a325fe0c7713cadac787526","observation_id":"e5653923-6cad-49e6-b744-aa156966ba53","resolution":{"observed_at":"2026-08-05T18:41:28.339596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:28.094755Z","title":"and Barab \\'a si, A.-L","venue":null,"work_id":"ae2e03d6-d3de-4717-97c1-e81d61c6c750","year":2016},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:18.995987Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:e65ffabb3bd91d8b52738109a92bb5c8f041433da7ca67583b79ca9ea6cf685c","observation_id":"cb11845d-db09-4426-93d0-4461a75b07aa","resolution":{"observed_at":"2026-08-05T18:41:28.180450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:27.933243Z","title":"C., and Bonvin, A","venue":null,"work_id":"83911539-389b-4465-aea1-dd6441dedae3","year":2023},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:19.100248Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:c7c81e6c7fdf951821a340bd88a7e6382303459b84b09a3b8b9726b51b9c01e4","observation_id":"d3059129-63b7-48eb-8e69-cd77f6749606","resolution":{"observed_at":"2026-08-05T18:41:28.011195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:27.754327Z","title":"Graph neural networks for materials science and chemistry","venue":null,"work_id":"a5fe30f6-897a-4630-b51a-1cced4fd0bcf","year":2022},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:19.239911Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:73e3764668278b388dc29a30bafa072259aefc64994d195f81b4a64855e08a08","observation_id":"1176c815-9369-41d7-901c-7515f1f0d8ee","resolution":{"observed_at":"2026-08-05T18:41:27.845898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.10993","last_updated":"2023-03-20T10:21:29Z","snapshot_observed_at":"2026-07-06T15:05:38.707313Z","submitted_at":"2023-03-20T10:21:29Z","title":"A Survey on Oversmoothing in Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.10993","snapshot_observed_at":"2026-08-05T18:41:19.310455Z","title":"K., Bronstein, M","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:19.310455Z"},"links":{"cited_paper":"/paper/2303.10993","citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:465bc0d3c7f77c095eaaff68d04e4078b3de4dc6340bc54450c72201dbe3689e","observation_id":"79923084-59cb-4ce2-85f7-723ec4572bd9","resolution":{"observed_at":"2026-08-05T18:41:19.310455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.04078","last_updated":"2020-10-16T05:22:01Z","snapshot_observed_at":"2026-08-05T16:05:37.590131Z","submitted_at":"2020-03-09T12:37:40Z","title":"A Survey on The Expressive Power of Graph Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.04078","snapshot_observed_at":"2026-08-05T18:41:19.411539Z","title":"A survey on the expressive power of graph neural networks","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:19.411539Z"},"links":{"cited_paper":"/paper/2003.04078","citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:baa8f254def5061838f51ab94e40cf7f1ce97e108eaaf2dead310a35feec9b3c","observation_id":"aa344fdd-ff60-4d8d-bd08-31daf1898e43","resolution":{"observed_at":"2026-08-05T18:41:19.411539Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:27.540191Z","title":"C., and Hagenbuchner, M","venue":null,"work_id":"0973524c-d538-4cf3-b9c2-adb4ae1ecd90","year":2018},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:19.495889Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:f6d598342acda421863e21b781e0e9b26406b554fb2f8fc79312504bec348a06","observation_id":"81e9ad3e-7541-4592-8557-6237a8ffe466","resolution":{"observed_at":"2026-08-05T18:41:27.667924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:27.321619Z","title":"Mspipe: Efficient temporal gnn training via staleness-aware pipeline","venue":null,"work_id":"48b395e6-6d08-490b-a9be-703be098c69a","year":2024},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:19.578816Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:1a7142928c33959023a07314b0716908c971ec60d9ebe46923bc3f96ea78ba7f","observation_id":"73c2989b-6f42-4492-bfd1-a86081a574f5","resolution":{"observed_at":"2026-08-05T18:41:27.455711Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:27.117230Z","title":"Spectral graph theory","venue":null,"work_id":"df5e335d-e55a-4f10-9710-b27313ace54b","year":2012},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:19.676845Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:29df30d4a6ea1e493fdb39ec373349827fba19d440315fc2dc37bd6ce6ee99e9","observation_id":"40b744e5-059b-4f35-b8a2-0d28227c42dd","resolution":{"observed_at":"2026-08-05T18:41:27.214384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:26.915748Z","title":"and Wu, C","venue":null,"work_id":"a5c772a5-18ce-45ec-a703-b1ccec910059","year":2024},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:19.758964Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:2008364006333dcd87b6e62faff19f648face6c3d7d5aa2c6c1c9503199f8e2b","observation_id":"15afde08-6813-4aea-8047-ea84afb57402","resolution":{"observed_at":"2026-08-05T18:41:27.002575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04284","last_updated":"2024-02-26T09:23:12Z","snapshot_observed_at":"2026-07-06T17:26:27.288834Z","submitted_at":"2024-02-06T01:34:56Z","title":"PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04284","snapshot_observed_at":"2026-08-05T18:41:19.900502Z","title":"Pres: Toward scalable memory-based dynamic graph neural networks, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:19.900502Z"},"links":{"cited_paper":"/paper/2402.04284","citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:67a88932b48a3587de371f5036df063ee9ffba34cac2ef396f8047ef867866de","observation_id":"92e6498c-eba6-4686-967f-a02e7d57c9c2","resolution":{"observed_at":"2026-08-05T18:41:19.900502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:26.696450Z","title":"and Liu, Y","venue":null,"work_id":"dbae2bd2-9cfb-4ca4-bd73-4995d0d36050","year":2023},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:19.995077Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:a36f3d5950a74a92325dcb6da7dd215070c5c61a46617043ca925a5afba47318","observation_id":"447ca4f4-9e87-4ebe-926b-edb5ce7a69cf","resolution":{"observed_at":"2026-08-05T18:41:26.799822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.14522","last_updated":"2022-11-12T16:11:19Z","snapshot_observed_at":"2026-07-06T12:13:06.745394Z","submitted_at":"2021-11-29T13:27:56Z","title":"Understanding over-squashing and bottlenecks on graphs via curvature","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.14522","snapshot_observed_at":"2026-08-05T18:41:20.063418Z","title":"P., Dong, X., and Bronstein, M","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:20.063418Z"},"links":{"cited_paper":"/paper/2111.14522","citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:c297bef6ff6d45f76e6c7fdb60a73323d443b479677372d3e6f75506985be6ee","observation_id":"15a16c80-36db-4337-b282-1849e5e9dc2d","resolution":{"observed_at":"2026-08-05T18:41:20.063418Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.14286","last_updated":"2022-12-06T00:16:17Z","snapshot_observed_at":"2026-08-03T21:07:11.749160Z","submitted_at":"2020-09-29T20:00:31Z","title":"Benign overfitting in ridge regression","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.14286","snapshot_observed_at":"2026-08-05T18:41:20.145764Z","title":"and Bartlett, P","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:20.145764Z"},"links":{"cited_paper":"/paper/2009.14286","citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:2f91dbd29efd287a0ee33ca5972315876f3669ce0d5ba27d0c5d367c95ad7b3f","observation_id":"6dbbba96-4dc5-4743-9b96-4e90ccab7aa7","resolution":{"observed_at":"2026-08-05T18:41:20.145764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:26.537662Z","title":"and Bartlett, P","venue":null,"work_id":"e74f64c6-8bf8-491a-82f5-285f40a5b7d7","year":2023},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:20.226983Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:9615c2996833668ed4323a0dc371923f8e8b789cdc08ec4a61cb1a1774c0976b","observation_id":"a7610be1-fbc2-49f1-9603-fea6af7ee04f","resolution":{"observed_at":"2026-08-05T18:41:26.604785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:26.367841Z","title":"Compound--protein interaction prediction with end-to-end learning of neural networks for graphs and sequences","venue":null,"work_id":"7cd95e34-59d1-4c06-85e4-ac8ce8b42704","year":2019},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:20.316583Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:1aac42f7793fc2b7f55074324b061547aadde414a6c52b63a2423c812ffb831e","observation_id":"d02a315b-dc1c-438d-8ede-a45a7a3e576d","resolution":{"observed_at":"2026-08-05T18:41:26.442209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:26.171902Z","title":"Graph spectra for complex networks","venue":null,"work_id":"f1cb824b-0a54-441f-a43e-c8b4535f4386","year":2023},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:20.380414Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:3b4093a99ad3212e95f15a77732ba7251c68c3b1135ede86d98f548614232416","observation_id":"37c9a8ed-8939-4207-aaea-b32a22589ce8","resolution":{"observed_at":"2026-08-05T18:41:26.278503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10903","last_updated":"2018-02-04T19:13:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-10-30T12:41:12Z","title":"Graph Attention Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10903","snapshot_observed_at":"2026-08-05T18:41:20.447935Z","title":"Graph attention networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:20.447935Z"},"links":{"cited_paper":"/paper/1710.10903","citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:ba512b8847b3b4b6722bd145b2772e09f28fd43cdbbc19b24d953095b9b61397","observation_id":"1fddeea9-231b-420b-bf10-74086982b9a1","resolution":{"observed_at":"2026-08-05T18:41:20.447935Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:25.934075Z","title":"and Zhang, Z.-L","venue":null,"work_id":"a9f222b1-8ed7-4985-b463-d4da53037085","year":2019},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:20.521066Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:40ab9f7fb5173efd77b4e942fdf69126898283507e099e908287597063e5c3dc","observation_id":"9afd97e2-5703-4776-9e88-5a16b68876f4","resolution":{"observed_at":"2026-08-05T18:41:26.044875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:25.699334Z","title":"and Xu, J","venue":null,"work_id":"4ceee331-8c76-45ae-8857-b3a36d749315","year":2020},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:20.580948Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:ca241071807d2c74f7859e527c7add5e4474ef39ef0e9d730cd10e739ef47513","observation_id":"574cb27d-ad19-4355-8d65-5aa1aed76c5e","resolution":{"observed_at":"2026-08-05T18:41:25.807860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:25.510317Z","title":"Simplifying graph convolutional networks","venue":null,"work_id":"242939fc-f75d-47ea-a83d-30381a8e410c","year":2019},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:20.648971Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:b07dc91504df5ce2225af277ef519194b54ae52ae32147590570807e11eb86e9","observation_id":"14d1500d-72a0-43a5-88d3-40837ab7c8b4","resolution":{"observed_at":"2026-08-05T18:41:25.611905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:25.280723Z","title":null,"venue":null,"work_id":"b3134c7a-7875-48d8-b4f0-b01135fa7f9a","year":2023},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:20.709432Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:dce992fa9a34e0640d8570902d17cc2e193c92ea1da28b917fdda2cfcfc0e113","observation_id":"359db9f4-25c9-4a73-b50a-4e96c82b6b06","resolution":{"observed_at":"2026-08-05T18:41:25.384887Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:25.121262Z","title":"Handling distribution shifts on graphs: An invariance perspective, 2022","venue":null,"work_id":"1272c8c1-7efd-45bf-a566-f49206d5e1d0","year":2022},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:20.767662Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:ea939745e2d02d6305dba5262f0656ed7e9f0ee7a3f3b0e86f8e706f6bd7c263","observation_id":"07ad373d-0b6e-4033-b6f7-94421808bd90","resolution":{"observed_at":"2026-08-05T18:41:25.193804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:24.951749Z","title":"B., and Fei-Fei, L","venue":null,"work_id":"40d8307e-320d-4c88-9690-ce2e78191635","year":2017},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:20.860253Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:ee3ee0f7f3eb6a12572debc174aabb94198837a5cacd4ee5864aed6611f7cc58","observation_id":"4d48f0fb-43eb-4288-94ea-70446bbb1f6c","resolution":{"observed_at":"2026-08-05T18:41:25.035167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:24.759012Z","title":"and Hsu, D","venue":null,"work_id":"eef8745f-6d6c-4a83-b582-9713140f0724","year":2019},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:20.923500Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:3c480a5c1daeb0b45d4c17e9b9d9e008bc639da584e4fba146ed55a662d70de1","observation_id":"4cafe744-3882-4d6c-bff3-3580771ef422","resolution":{"observed_at":"2026-08-05T18:41:24.860976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.00826","last_updated":"2019-02-22T19:15:54Z","snapshot_observed_at":"2026-07-06T07:05:24.565760Z","submitted_at":"2018-10-01T17:11:31Z","title":"How Powerful are Graph Neural Networks?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.00826","snapshot_observed_at":"2026-08-05T18:41:21.010034Z","title":"How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:21.010034Z"},"links":{"cited_paper":"/paper/1810.00826","citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:3fdf6e91685a007c69a399ab1b06844e5d42a3aeff9b36b39c02cfefe5161ff7","observation_id":"ce8ec512-70ca-4672-a803-6adc64f95142","resolution":{"observed_at":"2026-08-05T18:41:21.010034Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:24.530897Z","title":"Z., Guo, Z., Zhou, K., Zhang, W., and Liu, Z","venue":null,"work_id":"1dfe298c-7ef3-47cc-9156-ed02b5a0b387","year":2022},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:21.098054Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:13e92d2576b974f209b440304a9c405f4cb5265a13cb98df6fd7e91c1b49ba61","observation_id":"1f365408-59f6-4550-9ca3-0670c1b32de1","resolution":{"observed_at":"2026-08-05T18:41:24.653730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:24.218221Z","title":"Neural motifs: Scene graph parsing with global context, 2018","venue":null,"work_id":"21a9d2e7-33e7-4b07-a4a3-91a8bdca54e3","year":2018},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:21.189003Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:0a209adab9c9cc216d86c4ef06c904338f5d809e38dc27c0beff3c651a1cd15b","observation_id":"edbf1814-2eb2-4ebd-85da-cea663c105d4","resolution":{"observed_at":"2026-08-05T18:41:24.423340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.08235","last_updated":"2025-01-10T16:02:22Z","snapshot_observed_at":"2026-07-06T16:06:46.953622Z","submitted_at":"2023-08-16T09:12:21Z","title":"The Expressive Power of Graph Neural Networks: A Survey","version":2},"cited_work":{"arxiv_id":"2308.08235","doi":null,"metadata_source":"pith","pith_arxiv_id":"2308.08235","snapshot_observed_at":"2026-08-05T18:41:21.963304Z","title":"The Expressive Power of Graph Neural Networks: A Survey","venue":"cs.LG","work_id":"8b6d4214-55e1-4515-8fb1-7bf63c765273","year":2023},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:21.244827Z"},"links":{"cited_paper":"/paper/2308.08235","citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:93b91a38d5e3b26b91af3f9e718a4055b8188900c09aa57341e7ce7d8e3d9ef0","observation_id":"a1efca9d-d4a9-4483-a920-fc8ab25bc5b3","resolution":{"observed_at":"2026-08-05T18:41:22.035084Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:23.885827Z","title":"A complete expressiveness hierarchy for subgraph gnns via subgraph weisfeiler-lehman tests","venue":null,"work_id":"5629aeb5-b99f-47a3-8a46-eb37f3eb943a","year":2023},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:21.301239Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:f2523b51d5f960ced5e49ea1404b57480d159307b43c34da834ff6880a95c74b","observation_id":"0d3f6650-c556-4e11-a6a8-2fa24eb828b8","resolution":{"observed_at":"2026-08-05T18:41:24.030389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.09505","last_updated":"2024-02-11T03:44:23Z","snapshot_observed_at":"2026-08-01T14:37:45.825288Z","submitted_at":"2023-01-23T15:58:59Z","title":"Rethinking the Expressive Power of GNNs via Graph Biconnectivity","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.09505","snapshot_observed_at":"2026-08-05T18:41:21.350824Z","title":"Rethinking the expressive power of gnns via graph biconnectivity","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:21.350824Z"},"links":{"cited_paper":"/paper/2301.09505","citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:f6f43d4329e9cc0fc00cea3e87bdb4cfc83d9aa32af5f0519a9a063843a75dd3","observation_id":"8ff7cf5f-aa9a-4e6c-ba7f-281e8c901ca1","resolution":{"observed_at":"2026-08-05T18:41:21.350824Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:23.588073Z","title":null,"venue":null,"work_id":"0e990a52-942e-41eb-8f67-472d5d71e873","year":2022},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:21.440420Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:64cab2586a4b602dd2a1829cc4e80f7b1073d587d74c9ae4ac40b62a7f328afe","observation_id":"ce8ac558-f3bc-41c9-8744-2f61a21985a9","resolution":{"observed_at":"2026-08-05T18:41:23.717698Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:23.406092Z","title":"Shift-robust gnns: Overcoming the limitations of localized graph training data, 2021","venue":null,"work_id":"3c9f80ef-1c0d-40cd-beeb-7b5cb7273583","year":2021},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:21.507764Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:48a24575a18c5ef185e3682fc30ba906f30aeafac0f466c2af76e23328676ebd","observation_id":"50d3a005-442f-46ca-9185-a9380f7bcbbe","resolution":{"observed_at":"2026-08-05T18:41:23.481731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:23.246105Z","title":"P., and Kakade, S","venue":null,"work_id":"aed19788-fd5e-477f-8604-962c3f67cff0","year":2021},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:21.593164Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:555e3670e3ae0534a3ffc448f9751523015f639b309994a3d8c60e12b9536a8b","observation_id":"fe244dba-ab6b-4cb5-ad63-bad77c4b1cb6","resolution":{"observed_at":"2026-08-05T18:41:23.308716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:23.048932Z","title":"Benign overfitting of constant-stepsize sgd for linear regression","venue":null,"work_id":"12d27cf3-f95c-4ceb-9c87-60a20c82e45d","year":2021},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:21.689195Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:353f0243c22e00f17d93e141a454d911146bcffa1f36fc7f5128f46c77e7b63b","observation_id":"620fbac1-10b8-4818-a207-e92f3d3dbd27","resolution":{"observed_at":"2026-08-05T18:41:23.145889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:22.907255Z","title":null,"venue":null,"work_id":"4d5ef27b-4cc7-40b0-9d72-33b344da2ac4","year":2023},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:21.777430Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:1e1895b75161c6547eaa7be8e53fdaded2f6e1e4019ca602d5b69fbe7db41d78","observation_id":"b3290784-6d94-4c91-914c-0051c6cf463f","resolution":{"observed_at":"2026-08-05T18:41:22.960965Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:41:21.823852Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-05T18:41:21.823852Z"},"links":{"citing_paper":"/paper/2508.14338"},"observation_digest":"sha256:02915d100f8696a9b997b8a90ef1d124f0e15cab3c9cba13eed3244d3e7f3241","observation_id":"3acbd04a-3c27-46f1-951f-cd0f2aadb6d5","resolution":{"observed_at":"2026-08-05T18:41:21.823852Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2508.14338","last_updated":"2025-08-20T01:26:56Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-05T18:41:10.496868Z","submitted_at":"2025-08-20T01:26:56Z","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks"},"reference_resolution":{"displayed":79,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":20,"verified_exact":3,"verified_fuzzy":55},"total_outbound_references":79},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 0 inbound Pith citation observations for arXiv:2508.14338."}