{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HQAPORCEYO6333RLL3VSELZ6NL","short_pith_number":"pith:HQAPORCE","schema_version":"1.0","canonical_sha256":"3c00f74444c3bdbdee2b5eeb222f3e6afc720c748458ef0ba269a90f99c2d101","source":{"kind":"arxiv","id":"2501.16345","version":2},"attestation_state":"computed","paper":{"title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bhaskar Ray, Bishal Thapaliya, Esra Akbas, Jingyu Liu, Ram Sapkota, Vince Calhoun","submitted_at":"2025-01-17T20:21:31Z","abstract_excerpt":"Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the relationship between brain function and cognitive processes, as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this study, we introduce a novel attention mechanism for graphs with subnetworks, named Self-Clustering Graph Transformer (SCGT), designed to handle the issue of uniform node updates in graph transformers. By using static functional conn"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.16345","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-17T20:21:31Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"97451ec272851f15555581af0a2060b2a55b91c7c1796305fb4640f641549395","abstract_canon_sha256":"e0033cfe1e01b82d7081c589ab2e4e3839e28ff33199cf4118a5faf0b8d51d55"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:10:48.416349Z","signature_b64":"Fq1pGWVzjm57grvu3/EMcMH7MDDtuX/IRhf2sSg2PodGXQYwuPCieuSZbu0nksjn4ZWaxiUdl2LjTw5Zw4oLCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3c00f74444c3bdbdee2b5eeb222f3e6afc720c748458ef0ba269a90f99c2d101","last_reissued_at":"2026-07-05T10:10:48.415861Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:10:48.415861Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bhaskar Ray, Bishal Thapaliya, Esra Akbas, Jingyu Liu, Ram Sapkota, Vince Calhoun","submitted_at":"2025-01-17T20:21:31Z","abstract_excerpt":"Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the relationship between brain function and cognitive processes, as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this study, we introduce a novel attention mechanism for graphs with subnetworks, named Self-Clustering Graph Transformer (SCGT), designed to handle the issue of uniform node updates in graph transformers. By using static functional conn"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.16345","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.16345/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.16345","created_at":"2026-07-05T10:10:48.415919+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.16345v2","created_at":"2026-07-05T10:10:48.415919+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.16345","created_at":"2026-07-05T10:10:48.415919+00:00"},{"alias_kind":"pith_short_12","alias_value":"HQAPORCEYO63","created_at":"2026-07-05T10:10:48.415919+00:00"},{"alias_kind":"pith_short_16","alias_value":"HQAPORCEYO6333RL","created_at":"2026-07-05T10:10:48.415919+00:00"},{"alias_kind":"pith_short_8","alias_value":"HQAPORCE","created_at":"2026-07-05T10:10:48.415919+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.07081","citing_title":"Fast Clustering of Categorical Big Data","ref_index":41,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HQAPORCEYO6333RLL3VSELZ6NL","json":"https://pith.science/pith/HQAPORCEYO6333RLL3VSELZ6NL.json","graph_json":"https://pith.science/api/pith-number/HQAPORCEYO6333RLL3VSELZ6NL/graph.json","events_json":"https://pith.science/api/pith-number/HQAPORCEYO6333RLL3VSELZ6NL/events.json","paper":"https://pith.science/paper/HQAPORCE"},"agent_actions":{"view_html":"https://pith.science/pith/HQAPORCEYO6333RLL3VSELZ6NL","download_json":"https://pith.science/pith/HQAPORCEYO6333RLL3VSELZ6NL.json","view_paper":"https://pith.science/paper/HQAPORCE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.16345&json=true","fetch_graph":"https://pith.science/api/pith-number/HQAPORCEYO6333RLL3VSELZ6NL/graph.json","fetch_events":"https://pith.science/api/pith-number/HQAPORCEYO6333RLL3VSELZ6NL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HQAPORCEYO6333RLL3VSELZ6NL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HQAPORCEYO6333RLL3VSELZ6NL/action/storage_attestation","attest_author":"https://pith.science/pith/HQAPORCEYO6333RLL3VSELZ6NL/action/author_attestation","sign_citation":"https://pith.science/pith/HQAPORCEYO6333RLL3VSELZ6NL/action/citation_signature","submit_replication":"https://pith.science/pith/HQAPORCEYO6333RLL3VSELZ6NL/action/replication_record"}},"created_at":"2026-07-05T10:10:48.415919+00:00","updated_at":"2026-07-05T10:10:48.415919+00:00"}