{"as_of":"2026-08-07T20:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4de27af3b19235a6d875359db79b7d6aa4e259b48e80ac5db6fe6cc61523be7b","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":25,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:11:39.160435Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T08:49:42.010484Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2006.13318","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-07-04T08:49:42.010484Z","title":"A Note on Over-Smoothing for Graph Neural Networks, June 2020","venue":null,"work_id":"357166af-f327-48bd-8c0f-de9fc925688c","year":2006},"citing_paper":{"arxiv_id":"2409.08036","last_updated":"2026-04-16T21:00:42Z","snapshot_observed_at":"2026-08-02T15:48:28.524877Z","submitted_at":"2024-09-12T13:38:08Z","title":"Heterogeneous Sheaf Neural Networks","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-23T20:57:34.082406Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2409.08036"},"observation_digest":"sha256:9a2a5f216128e88bc469cad829471aba2ee116ed6039e92657c5193a7ec7b24b","observation_id":"abe5615e-6397-4a23-bc90-d6eccbec1f01","resolution":{"observed_at":"2026-05-23T20:58:26.730276Z","resolver_source":"arxiv_id","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":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-08-07T13:11:39.160435Z","title":"org/abs/2006.13318","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.22504","last_updated":"2025-05-28T15:52:22Z","snapshot_observed_at":"2026-08-07T13:03:15.901609Z","submitted_at":"2025-05-28T15:52:22Z","title":"Geometric GNNs for Charged Particle Tracking at GlueX","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T13:11:39.160435Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2505.22504"},"observation_digest":"sha256:bcc899ad26663183100de5bd9d2b30cc084e15017b015ba09fbedb654fb32637","observation_id":"a414c8a2-beef-4374-a888-43a4cf08a384","resolution":{"observed_at":"2026-08-07T13:11:39.160435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-08-07T01:07:34.736049Z","title":"A note on over-smoothing for graph neural networks","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-07T00:59:56.868062Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.736049Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:ec899b4999163f5ef2fc2da05eeaab94eee4e3cd50a69ddfca558a6492a7f5d2","observation_id":"622366f4-bb0c-4cc6-baee-655ff236e03d","resolution":{"observed_at":"2026-08-07T01:07:34.736049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-08-06T21:35:07.857578Z","title":"A note on over-smoothing for graph neural networks","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.24018","last_updated":"2025-06-30T16:22:15Z","snapshot_observed_at":"2026-08-07T10:27:10.133933Z","submitted_at":"2025-06-30T16:22:15Z","title":"Bridging Theory and Practice in Link Representation with Graph Neural Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:35:07.857578Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2506.24018"},"observation_digest":"sha256:b591a6f692a3b66d1aa42caab4483157c89feb57d08fbc517c1f926958e05bf6","observation_id":"322575a8-17f1-45a1-834a-d280c63a48e1","resolution":{"observed_at":"2026-08-06T21:35:07.857578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-08-05T23:39:46.043567Z","title":"A note on over-smoothing for graph neural networks.arXiv preprint arXiv:2006.13318, 2020","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2508.05070","last_updated":"2025-08-07T06:44:01Z","snapshot_observed_at":"2026-08-06T10:36:57.601907Z","submitted_at":"2025-08-07T06:44:01Z","title":"TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T23:39:46.043567Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2508.05070"},"observation_digest":"sha256:199e5ac3a4b765622a5afd1b3459b0d20a1a076aa8a0a6963610fd25b7fa6b6c","observation_id":"40db5b14-6da4-4aee-b708-3ace3a9ae556","resolution":{"observed_at":"2026-08-05T23:39:46.043567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-08-05T10:25:45.787593Z","title":"Cai and Y","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2509.04178","last_updated":"2025-09-04T12:53:37Z","snapshot_observed_at":"2026-08-05T10:25:44.604975Z","submitted_at":"2025-09-04T12:53:37Z","title":"Comment on \"A Note on Over-Smoothing for Graph Neural Networks\"","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T10:25:45.787593Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2509.04178"},"observation_digest":"sha256:a1b12e9dfe5c0303a83191db5b9a7f73ac16d80a4da217fecf52a2a6db085e1f","observation_id":"bdabdcd1-bff2-4c12-bafc-04c07181b95e","resolution":{"observed_at":"2026-08-05T10:25:45.787593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2006.13318","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-07-04T08:49:42.010484Z","title":"A Note on Over-Smoothing for Graph Neural Networks, June 2020","venue":null,"work_id":"357166af-f327-48bd-8c0f-de9fc925688c","year":2006},"citing_paper":{"arxiv_id":"2511.06443","last_updated":"2025-11-09T16:25:37Z","snapshot_observed_at":"2026-08-01T02:52:13.483898Z","submitted_at":"2025-11-09T16:25:37Z","title":"How Wide and How Deep? Mitigating Over-Squashing of GNNs via Channel Capacity Constrained Estimation","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-17T23:23:07.672908Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2511.06443"},"observation_digest":"sha256:1461d7efb24b6b8fbe5f1ad06ae56952e06c8cf89449dc8cba358e887e92014e","observation_id":"cf700b82-eb71-4765-824d-87dffa65fbd7","resolution":{"observed_at":"2026-05-17T23:25:28.625201Z","resolver_source":"arxiv_id","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":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2006.13318","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-07-04T08:49:42.010484Z","title":"A Note on Over-Smoothing for Graph Neural Networks, June 2020","venue":null,"work_id":"357166af-f327-48bd-8c0f-de9fc925688c","year":2006},"citing_paper":{"arxiv_id":"2601.01123","last_updated":"2026-05-17T16:57:28Z","snapshot_observed_at":"2026-08-06T06:45:06.116751Z","submitted_at":"2026-01-03T08:51:38Z","title":"Learning from Historical Activations in Graph Neural Networks","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-21T17:14:52.859473Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2601.01123"},"observation_digest":"sha256:8a2e0716a9c4dd028c4fc8cd4972fd11f9f47b2dd0c5df29e6d30a106772601c","observation_id":"26bad750-b3ec-4d1f-afbc-3a9e7700dd7c","resolution":{"observed_at":"2026-05-21T17:15:25.467255Z","resolver_source":"arxiv_id","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":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2006.13318","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-07-04T08:49:42.010484Z","title":"A Note on Over-Smoothing for Graph Neural Networks, June 2020","venue":null,"work_id":"357166af-f327-48bd-8c0f-de9fc925688c","year":2006},"citing_paper":{"arxiv_id":"2602.05352","last_updated":"2026-05-12T03:14:17Z","snapshot_observed_at":"2026-07-06T22:44:37.993093Z","submitted_at":"2026-02-05T06:23:25Z","title":"Smoothness Errors in Dynamics Models and How to Avoid Them","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-16T07:23:07.669607Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2602.05352"},"observation_digest":"sha256:07500cb0edb1c5a5a4d109e10f8b4b465d30db8003492f90a9a7eee843e07f42","observation_id":"c6b523cf-922b-4a1c-ba8b-6bc3d9b57137","resolution":{"observed_at":"2026-05-16T07:27:32.243070Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-08-02T22:56:38.772674Z","title":"and Wang, Y","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.15634","last_updated":"2026-05-29T12:35:53Z","snapshot_observed_at":"2026-08-02T22:56:36.112740Z","submitted_at":"2026-02-17T15:03:28Z","title":"Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priors","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-02T22:56:38.772674Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2602.15634"},"observation_digest":"sha256:adc5fbd6b5a69e593790edafde0480bf6ebd97222e918fb75b2535a3714f052d","observation_id":"dd899da5-6fbc-47cc-b94d-c206a2645be5","resolution":{"observed_at":"2026-08-02T22:56:38.772674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2006.13318","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-07-04T08:49:42.010484Z","title":"A Note on Over-Smoothing for Graph Neural Networks, June 2020","venue":null,"work_id":"357166af-f327-48bd-8c0f-de9fc925688c","year":2006},"citing_paper":{"arxiv_id":"2604.11791","last_updated":"2026-04-13T17:55:36Z","snapshot_observed_at":"2026-08-02T14:25:36.633868Z","submitted_at":"2026-04-13T17:55:36Z","title":"A Mechanistic Analysis of Looped Reasoning Language Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T15:53:19.680424Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2604.11791"},"observation_digest":"sha256:5800f838d07bf8f2f5dd70b5c271473621fd03b0d46c9b483f600149353c6d89","observation_id":"42219ca9-eb8d-4fdd-be3b-a3fd83f865d7","resolution":{"observed_at":"2026-05-11T09:41:03.018771Z","resolver_source":"arxiv_id","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":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2006.13318","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-07-04T08:49:42.010484Z","title":"A Note on Over-Smoothing for Graph Neural Networks, June 2020","venue":null,"work_id":"357166af-f327-48bd-8c0f-de9fc925688c","year":2006},"citing_paper":{"arxiv_id":"2604.19028","last_updated":"2026-04-21T03:23:34Z","snapshot_observed_at":"2026-07-06T23:05:44.499005Z","submitted_at":"2026-04-21T03:23:34Z","title":"Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors","version":1},"reference_index":263,"source":"arxiv_source","source_observed_at":"2026-05-10T03:50:44.626261Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2604.19028"},"observation_digest":"sha256:8e803f3e09e4b601c9dbd2a31c93cd4ec9ea78ed9d263e133e153fd61db81063","observation_id":"7d8c3f36-9bb2-404e-9eed-3bd7dda70c93","resolution":{"observed_at":"2026-05-11T12:21:04.805127Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2006.13318","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-07-04T08:49:42.010484Z","title":"A Note on Over-Smoothing for Graph Neural Networks, June 2020","venue":null,"work_id":"357166af-f327-48bd-8c0f-de9fc925688c","year":2006},"citing_paper":{"arxiv_id":"2604.23324","last_updated":"2026-04-25T14:25:02Z","snapshot_observed_at":"2026-07-06T23:09:34.300163Z","submitted_at":"2026-04-25T14:25:02Z","title":"Layer Embedding Deep Fusion Graph Neural Network","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-08T08:22:06.951781Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2604.23324"},"observation_digest":"sha256:1e25aef4b6162e20bedd4a5690b6a532fe20c9d9893b3ee379294eb9ea82d5df","observation_id":"7a6add99-3e9c-41b5-af6f-c1cf77ab9889","resolution":{"observed_at":"2026-05-11T20:41:10.214896Z","resolver_source":"arxiv_id","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":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2006.13318","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-07-04T08:49:42.010484Z","title":"A Note on Over-Smoothing for Graph Neural Networks, June 2020","venue":null,"work_id":"357166af-f327-48bd-8c0f-de9fc925688c","year":2006},"citing_paper":{"arxiv_id":"2605.03861","last_updated":"2026-05-05T15:23:37Z","snapshot_observed_at":"2026-07-31T12:02:20.453541Z","submitted_at":"2026-05-05T15:23:37Z","title":"Aspect-Aware Content-Based Recommendations for Mathematical Research Papers","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-07T14:19:18.282566Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2605.03861"},"observation_digest":"sha256:c2f61b0254c9c4ed1e3b9cae7be5d288f3bc7a057b1d102a0fdad4918a2760ee","observation_id":"01bbc750-108e-45f2-ae09-0f31b308cec2","resolution":{"observed_at":"2026-05-12T00:46:12.998127Z","resolver_source":"arxiv_id","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":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2006.13318","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-07-04T08:49:42.010484Z","title":"A Note on Over-Smoothing for Graph Neural Networks, June 2020","venue":null,"work_id":"357166af-f327-48bd-8c0f-de9fc925688c","year":2006},"citing_paper":{"arxiv_id":"2605.08391","last_updated":"2026-05-19T01:58:16Z","snapshot_observed_at":"2026-07-06T23:20:38.385189Z","submitted_at":"2026-05-08T19:00:34Z","title":"SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-12T01:31:19.576223Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2605.08391"},"observation_digest":"sha256:87577b0e8718e6dbf97207c18aa02f876a232b78e25cbd1cffe762219ace717b","observation_id":"09412db8-f76b-4fe4-b271-d250206a6b8e","resolution":{"observed_at":"2026-05-12T07:56:27.392442Z","resolver_source":"arxiv_id","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":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2006.13318","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-07-04T08:49:42.010484Z","title":"A Note on Over-Smoothing for Graph Neural Networks, June 2020","venue":null,"work_id":"357166af-f327-48bd-8c0f-de9fc925688c","year":2006},"citing_paper":{"arxiv_id":"2605.08391","last_updated":"2026-05-19T01:58:16Z","snapshot_observed_at":"2026-07-06T23:20:38.385189Z","submitted_at":"2026-05-08T19:00:34Z","title":"SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-20T22:34:46.440511Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2605.08391"},"observation_digest":"sha256:5ebe93276183a48871085a6cd797477258c6847614890c391296f5c888b3ccba","observation_id":"8b6f1b69-1278-4e97-9229-098cf290e339","resolution":{"observed_at":"2026-05-20T22:39:10.766166Z","resolver_source":"arxiv_id","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":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2006.13318","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-07-04T08:49:42.010484Z","title":"A Note on Over-Smoothing for Graph Neural Networks, June 2020","venue":null,"work_id":"357166af-f327-48bd-8c0f-de9fc925688c","year":2006},"citing_paper":{"arxiv_id":"2605.13834","last_updated":"2026-05-29T19:42:16Z","snapshot_observed_at":"2026-07-06T23:25:21.032938Z","submitted_at":"2026-05-13T17:56:23Z","title":"Topology-Preserving Neural Operator Learning via Hodge Decomposition","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-14T19:05:31.289091Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2605.13834"},"observation_digest":"sha256:e7cd77e343d995968bfb375397b0b5431df5c93654bb86c0ca09a26af8e6382b","observation_id":"8fb699ff-ca29-4301-bd1d-7965373fe204","resolution":{"observed_at":"2026-05-14T19:07:51.550122Z","resolver_source":"arxiv_id","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":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2006.13318","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-07-04T08:49:42.010484Z","title":"A Note on Over-Smoothing for Graph Neural Networks, June 2020","venue":null,"work_id":"357166af-f327-48bd-8c0f-de9fc925688c","year":2006},"citing_paper":{"arxiv_id":"2605.13834","last_updated":"2026-05-29T19:42:16Z","snapshot_observed_at":"2026-07-06T23:25:21.032938Z","submitted_at":"2026-05-13T17:56:23Z","title":"Topology-Preserving Neural Operator Learning via Hodge Decomposition","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-06-30T21:43:50.425136Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2605.13834"},"observation_digest":"sha256:532b85460ea7dbc44ce1f4e80f39b4660189f822d3daef53a878ef3bdc3d36ee","observation_id":"4326657b-d397-4c87-85c7-a7569310223e","resolution":{"observed_at":"2026-06-30T21:45:05.601379Z","resolver_source":"arxiv_id","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":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2006.13318","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-07-04T08:49:42.010484Z","title":"A Note on Over-Smoothing for Graph Neural Networks, June 2020","venue":null,"work_id":"357166af-f327-48bd-8c0f-de9fc925688c","year":2006},"citing_paper":{"arxiv_id":"2605.15524","last_updated":"2026-05-15T01:44:31Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-15T01:44:31Z","title":"Neural Point-Forms","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-19T15:00:51.826915Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2605.15524"},"observation_digest":"sha256:08feb927d6201d702139e4d73551f591570a9666063beaa54b818e4eacd0c5ce","observation_id":"42019845-f612-4759-8bf2-5269099c75f1","resolution":{"observed_at":"2026-05-19T15:02:36.572074Z","resolver_source":"arxiv_id","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":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2006.13318","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-07-04T08:49:42.010484Z","title":"A Note on Over-Smoothing for Graph Neural Networks, June 2020","venue":null,"work_id":"357166af-f327-48bd-8c0f-de9fc925688c","year":2006},"citing_paper":{"arxiv_id":"2605.18387","last_updated":"2026-05-18T13:31:21Z","snapshot_observed_at":"2026-07-06T23:29:14.916114Z","submitted_at":"2026-05-18T13:31:21Z","title":"Graph Hierarchical Recurrence for Long-Range Generalization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-20T13:06:49.896124Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2605.18387"},"observation_digest":"sha256:979453ccfd74bf279061c1cb974e95977cbe4e5f868c6f7d1d21d3ca766e03e6","observation_id":"c48fbcdb-bac9-462b-8abd-88a2b610151a","resolution":{"observed_at":"2026-05-20T13:08:17.756805Z","resolver_source":"arxiv_id","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":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2006.13318","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-07-04T08:49:42.010484Z","title":"A Note on Over-Smoothing for Graph Neural Networks, June 2020","venue":null,"work_id":"357166af-f327-48bd-8c0f-de9fc925688c","year":2006},"citing_paper":{"arxiv_id":"2606.19185","last_updated":"2026-06-17T15:24:37Z","snapshot_observed_at":"2026-07-06T23:54:32.815760Z","submitted_at":"2026-06-17T15:24:37Z","title":"AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion Network","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-26T21:29:20.129182Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2606.19185"},"observation_digest":"sha256:31b094a625901c348b2824aecc6dae7e2c5e3fafdf953cdbcac5d3c4cf005875","observation_id":"1102a938-3f53-40aa-aa7d-d66f0f3feed9","resolution":{"observed_at":"2026-07-04T00:09:14.385842Z","resolver_source":"arxiv_id","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":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2006.13318","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-07-04T08:49:42.010484Z","title":"A Note on Over-Smoothing for Graph Neural Networks, June 2020","venue":null,"work_id":"357166af-f327-48bd-8c0f-de9fc925688c","year":2006},"citing_paper":{"arxiv_id":"2606.22429","last_updated":"2026-06-21T10:40:33Z","snapshot_observed_at":"2026-08-07T18:47:13.612767Z","submitted_at":"2026-06-21T10:40:33Z","title":"Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-06-26T10:59:25.867813Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2606.22429"},"observation_digest":"sha256:d26aab7f979306aef9452ebde9684e1d313f92217f17164ae0197db1983bf7d0","observation_id":"8b90488a-b86a-48ca-98e5-3e1de74bd5c0","resolution":{"observed_at":"2026-07-04T08:49:42.012875Z","resolver_source":"arxiv_id","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":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-08-01T22:29:30.198124Z","title":"A note on over-smoothing for graph neural networks,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.15773","last_updated":"2026-07-17T09:04:22Z","snapshot_observed_at":"2026-08-07T05:59:18.692446Z","submitted_at":"2026-07-17T09:04:22Z","title":"From Diffusion to Reaction-Diffusion: A Dynamical-Systems View of Oversmoothing in Hypergraph Neural Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T22:29:30.198124Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2607.15773"},"observation_digest":"sha256:b016aa943eec4ad846a0d2eddf7459d346bcae321510b5370cc94a7e328f641f","observation_id":"dcdee1e8-e9d9-4186-9584-1c53a58a0f98","resolution":{"observed_at":"2026-08-01T22:29:30.198124Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-08-01T06:31:19.346482Z","title":"A note on over-smoothing for graph neural networks, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:19.346482Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:2308921c2be82522cc1c7b9b5211c86e8ad305cdb8b1c25ee0ba186a44ffef24","observation_id":"67464bd9-51e9-4640-9d03-397e515783e7","resolution":{"observed_at":"2026-08-01T06:31:19.346482Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-08-01T05:00:11.704197Z","title":"A note on over-smoothing for graph neural networks.arXiv preprint arXiv:2006.13318, 2020","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.22381","last_updated":"2026-07-24T15:07:37Z","snapshot_observed_at":"2026-08-05T15:53:53.177448Z","submitted_at":"2026-07-24T15:07:37Z","title":"Local-Global Geometric Insights for Graph Neural Networks via Entropic Curvature","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T05:00:11.704197Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2607.22381"},"observation_digest":"sha256:8a604721355c76f49d9ff62d10c70291ee7689107adc33f464184555a238b9f5","observation_id":"c40c02ee-b0aa-4b7a-855a-543092a66d82","resolution":{"observed_at":"2026-08-01T05:00:11.704197Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2006.13318/citation-record","integrity":"/paper/2006.13318/integrity","json":"/paper/2006.13318/citation-record.json","paper":"/paper/2006.13318"},"outbound":[],"paper":{"arxiv_id":"2006.13318","last_updated":"2020-06-23T20:36:56Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+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 0 of 0 outbound references and 25 inbound Pith citation observations for arXiv:2006.13318."}