{"as_of":"2026-08-22T12:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7c6f4089764a3e64fa92b4e7d4c2d62cbfb2e463d6166ff5fdaddaa6222ceeb6","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T19:15:06.444524Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T13:54:36.478500Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-10T05:30:23.456663Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-10T05:30:23.456663Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"cited_work":{"arxiv_id":"2501.16345","doi":"10.48550/arxiv.2501.16345","metadata_source":"pith","pith_arxiv_id":"2501.16345","snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","venue":"cs.LG","work_id":"9a0313cd-df1d-4f6b-a249-f45610a6dbb5","year":2025},"citing_paper":{"arxiv_id":"2502.07081","last_updated":"2025-02-15T20:57:13Z","snapshot_observed_at":"2026-08-09T14:46:40.705209Z","submitted_at":"2025-02-10T22:19:08Z","title":"Fast Clustering of Categorical Big Data","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-08T13:54:36.478500Z"},"links":{"cited_paper":"/paper/2501.16345","citing_paper":"/paper/2502.07081"},"observation_digest":"sha256:543a5b60244fdca9c0358bb498bf7fd99148317a7e58002f2140428a5c2c3b15","observation_id":"9f51d202-3873-45be-8569-bf6e9f2382c2","resolution":{"observed_at":"2026-08-08T13:54:36.537411Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.16345/citation-record","integrity":"/paper/2501.16345/integrity","json":"/paper/2501.16345/citation-record.json","paper":"/paper/2501.16345"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T19:15:07.231947Z","title":"Resting-state fMRI: A review of methods and clinical applications,","venue":null,"work_id":"29d39c20-a007-4a5f-a598-75970a1675b9","year":2012},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.247637Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:807b9c3a8635db1d468415b502f973c197e5703c6c8a359a6355ce4a85bc5892","observation_id":"1dcc10e1-e82b-4bf6-8687-c638c299704e","resolution":{"observed_at":"2026-08-10T19:15:07.251414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:07.188447Z","title":"Multimodal imaging feature extraction with reference canonical correlation analysis underlying intelligence,","venue":null,"work_id":"1f414433-4a7c-4621-8bca-26130dd3d19d","year":2024},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.259101Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:8bfc012bb1b8fa3f5337ea32b07abd7765286203a5d22905c2511e5761869dd6","observation_id":"c9b1e6b1-11ac-4af5-a3e3-588aad356bae","resolution":{"observed_at":"2026-08-10T19:15:07.198652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:07.160969Z","title":"Effective training strategy for NN models of working memory classification with limited samples,","venue":null,"work_id":"cfa9047b-e9de-4046-9339-ba72e0177112","year":2023},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.268539Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:d9d03e21007e1fce545daec38fa440b24503c66a49206748385a968b32173708","observation_id":"3d507056-5853-4f02-af66-fd734f6b1d82","resolution":{"observed_at":"2026-08-10T19:15:07.169779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:07.129987Z","title":"Replication and refinement of brain age model for adolescent development,","venue":null,"work_id":"54182dc0-a1a1-4543-bdcb-d8d6a9a26846","year":2023},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.277585Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:b1d16df3b301cb73e180342ec8eaead5e5b78b469718cfa094241b20c26c47d4","observation_id":"40b03ae6-fbd4-4d67-b481-6ae1496d2603","resolution":{"observed_at":"2026-08-10T19:15:07.139091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:07.073191Z","title":"Dsam: A deep learning framework for analyzing temporal and spatial dynamics in brain networks,","venue":null,"work_id":"b6e8c24b-933a-451b-99d5-74516998c908","year":2025},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.284474Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:728d473ba250bc334d750e39926ac35b85729f5c88e7be85780f27fb095f9bc7","observation_id":"b345e726-c734-4a2c-9483-924257c1a1c0","resolution":{"observed_at":"2026-08-10T19:15:07.092764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:07.016208Z","title":"Fluid and flexible minds: Intelligence reflects synchrony in the brain’s intrinsic network architecture,","venue":null,"work_id":"28003358-a50d-4813-891e-7417f7a0a51e","year":2017},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.295466Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:fe955c196ecd0b3c21ab372ef4bd65943bff597cd531ebac9a144be61ad8ab49","observation_id":"21d2aa9e-a453-4e9b-ab18-9cd6e98f759b","resolution":{"observed_at":"2026-08-10T19:15:07.039170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:06.967243Z","title":"Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics,","venue":null,"work_id":"afbf1eb0-d088-48da-af68-b3b815b25fca","year":2020},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.310590Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:9d2bb2a0a4b16f2415249604b94ba86aefdee7c5e4b3f2b9877caf5a58f15ca1","observation_id":"835bfb2c-9e51-4cf2-928c-a5ce4acd34e3","resolution":{"observed_at":"2026-08-10T19:15:06.980004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:06.899579Z","title":"A distributed brain network predicts general intelligence from resting- state human neuroimaging data,","venue":null,"work_id":"99d931bd-e409-41c5-acb2-fda3b566c50d","year":2018},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.325589Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:869d82e85b94cc6d84e6c7807cbdeefac69ce5fc61af56e5bd2195bf409206ad","observation_id":"fff2b7aa-ec54-4314-aa6e-f335fb95c8eb","resolution":{"observed_at":"2026-08-10T19:15:06.916272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:06.859700Z","title":"Functional connectomics from resting-state fMRI,","venue":null,"work_id":"4fe9e991-636a-4196-abe2-29f0e143d528","year":2013},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.337640Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:ede56f19307395337ad532aeacf70d2fa73c16ebd1912c8eee5d82837dbc02f9","observation_id":"58c985f6-b487-4f5b-84be-3f4cda517e68","resolution":{"observed_at":"2026-08-10T19:15:06.868360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:06.835600Z","title":"Graph-based deep learning models in the prediction of early-stage alzheimers,","venue":null,"work_id":"8d2c517e-be8b-4560-a904-b84dae6f1672","year":2024},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.351507Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:607ddc5bed41bb0a1a0d8d1d44883381268e00ab98cf24553862a12e3cddcd47","observation_id":"8b719072-18de-4a03-88f0-264039a1d1b3","resolution":{"observed_at":"2026-08-10T19:15:06.842756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:06.810722Z","title":"BrainGNN: Interpretable brain graph neural network for fMRI analysis,","venue":null,"work_id":"aa5896bc-d453-4d9e-b1c3-c4dfdfa4781a","year":2021},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.357825Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:bd34adb02cfa8db8b0291dc9e469b295b7f61fa7a5fc3aaf891aaa999eaa8e69","observation_id":"bc59debf-bcbc-40bb-94ca-d460e2c09c90","resolution":{"observed_at":"2026-08-10T19:15:06.818386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:06.778728Z","title":"Brain networks and intelligence: A graph neural network based approach to resting state fmri data,","venue":null,"work_id":"6f860e11-84f4-42bf-8ec0-f70b273b6616","year":2025},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.366446Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:0ed48d8a33035fc63f00fd76b2e4b7799ba7ddff2666b1d8dff5a6875983cadb","observation_id":"9fa8a831-9584-4728-8f5a-91fc1ab5b1c4","resolution":{"observed_at":"2026-08-10T19:15:06.791220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:06.730683Z","title":"NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders,","venue":null,"work_id":"673a166b-9232-498a-b47d-ca52469fb47f","year":2020},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.389021Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:16c1bf8df66b8523a05c725683383375e88dc60b34a532b60d65a0ca58ffbac1","observation_id":"1f6054d2-012e-44a7-a5e0-b07220ba2d24","resolution":{"observed_at":"2026-08-10T19:15:06.745566Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:06.695220Z","title":"A generalization of transformer networks to graphs,","venue":null,"work_id":"7393551e-737d-469e-804e-169116973ed0","year":2020},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.404892Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:95ce4b120b956ca437f329cf327ed718a4cfb470d923738acfd7a032b28422d4","observation_id":"5397c769-dfc9-4ed0-9ad8-87f26dbc19fc","resolution":{"observed_at":"2026-08-10T19:15:06.704773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:06.652657Z","title":"Pytorch: An imperative style, high-performance deep learning library,","venue":null,"work_id":"2a34b037-90bf-41dc-92f8-ecf298566216","year":2019},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.410414Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:f9139863cd8122ce00a75118a633f64051aa09ebe0d0e6e2116bfe332c8601a2","observation_id":"a63f5555-b4b9-42ac-9efb-0fa1354f030f","resolution":{"observed_at":"2026-08-10T19:15:06.664881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:06.619246Z","title":"Fast graph representation learning with pytorch geometric,","venue":null,"work_id":"7c89cb24-8d17-4239-b707-33ffbb826e71","year":2019},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.416637Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:f858a950f5959f462697095757c4cfc9fa2beea1cc64be85a5eecb2860f510e3","observation_id":"08c0cb5c-f493-4eb9-b246-96833d2b4c4e","resolution":{"observed_at":"2026-08-10T19:15:06.630981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:06.587490Z","title":"The organization of the human cerebral cortex estimated by intrinsic functional connectivity,","venue":null,"work_id":"8d637ff5-29c5-477d-9c9c-0b3705fc1f1b","year":2011},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.430741Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:066f78224fba7f25d9832aa7b070b9a22ef17ef8757b1012be110ab2b35da514","observation_id":"85f953fe-c87a-4782-baa3-78e4488b87ff","resolution":{"observed_at":"2026-08-10T19:15:06.596409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:06.544926Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":"0443bfae-bc8b-46ba-a556-535998a4a609","year":2014},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.438012Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:850d8c48925f89467b8c8c4b943e52a0d73bff797f53893eca94f6bc8e47f167","observation_id":"fdf2c6ba-a928-4404-8864-2fe8a1e6243c","resolution":{"observed_at":"2026-08-10T19:15:06.555926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-10T19:15:06.510733Z","title":"Brainnetcnn: Convolutional neural networks for brain networks; towards predicting neurodevelopment,","venue":null,"work_id":"6385e547-815b-407d-8464-e0ba6cf1cd2b","year":2017},"citing_paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T19:15:06.444524Z"},"links":{"citing_paper":"/paper/2501.16345"},"observation_digest":"sha256:8647bc94b8b2f372ceb0ea6c629d324f1a07ce1527223ab15bfd94a9daae8bf0","observation_id":"2ae6d7a8-5efa-4928-935d-dca3871d8f8a","resolution":{"observed_at":"2026-08-10T19:15:06.522706Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.16345","last_updated":"2025-02-07T08:57:37Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T10:23:56.051279Z","submitted_at":"2025-01-17T20:21:31Z","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":19},"total_outbound_references":19},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2501.16345."}