{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TAVIL74N3QQX3WLNWVSHP5M2NP","short_pith_number":"pith:TAVIL74N","schema_version":"1.0","canonical_sha256":"982a85ff8ddc217dd96db56477f59a6bdd0bed71527091ae967c84bc1ebc385b","source":{"kind":"arxiv","id":"2412.06212","version":1},"attestation_state":"computed","paper":{"title":"A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Feng Zheng, Guodong Liu, Lei Yang, Liang Zhan, Runxue Bao, Weiwen Jiang, Yanfu Zhang, Yawen Wu, Zhepeng Wang","submitted_at":"2024-12-09T05:16:32Z","abstract_excerpt":"Graph neural networks (GNNs) are powerful machine learning models designed to handle irregularly structured data. However, their generic design often proves inadequate for analyzing brain connectomes in Alzheimer's Disease (AD), highlighting the need to incorporate domain knowledge for optimal performance. Infusing AD-related knowledge into GNNs is a complicated task. Existing methods typically rely on collaboration between computer scientists and domain experts, which can be both time-intensive and resource-demanding. To address these limitations, this paper presents a novel self-guided, know"},"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":"2412.06212","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-09T05:16:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"511850e0846d1cea3044509f4a1e7e33b1ebb97e20c58f690c32d1bb3622362c","abstract_canon_sha256":"c4dc45f02d3eb0404ece1cbd90e03e631aa38c6ba741d84d940740eadfcdf87d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:46:17.245135Z","signature_b64":"wXkQHBwr6UQdOK8PSgbyFUUbo5CLMzf/Xgzr5IWwxE3B6M+w7nVABVu7eh34RxESZtjz0vmFJ6zbfTWGStyWCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"982a85ff8ddc217dd96db56477f59a6bdd0bed71527091ae967c84bc1ebc385b","last_reissued_at":"2026-07-05T09:46:17.240781Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:46:17.240781Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Feng Zheng, Guodong Liu, Lei Yang, Liang Zhan, Runxue Bao, Weiwen Jiang, Yanfu Zhang, Yawen Wu, Zhepeng Wang","submitted_at":"2024-12-09T05:16:32Z","abstract_excerpt":"Graph neural networks (GNNs) are powerful machine learning models designed to handle irregularly structured data. However, their generic design often proves inadequate for analyzing brain connectomes in Alzheimer's Disease (AD), highlighting the need to incorporate domain knowledge for optimal performance. Infusing AD-related knowledge into GNNs is a complicated task. Existing methods typically rely on collaboration between computer scientists and domain experts, which can be both time-intensive and resource-demanding. To address these limitations, this paper presents a novel self-guided, know"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.06212","kind":"arxiv","version":1},"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/2412.06212/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":"2412.06212","created_at":"2026-07-05T09:46:17.244326+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.06212v1","created_at":"2026-07-05T09:46:17.244326+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.06212","created_at":"2026-07-05T09:46:17.244326+00:00"},{"alias_kind":"pith_short_12","alias_value":"TAVIL74N3QQX","created_at":"2026-07-05T09:46:17.244326+00:00"},{"alias_kind":"pith_short_16","alias_value":"TAVIL74N3QQX3WLN","created_at":"2026-07-05T09:46:17.244326+00:00"},{"alias_kind":"pith_short_8","alias_value":"TAVIL74N","created_at":"2026-07-05T09:46:17.244326+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TAVIL74N3QQX3WLNWVSHP5M2NP","json":"https://pith.science/pith/TAVIL74N3QQX3WLNWVSHP5M2NP.json","graph_json":"https://pith.science/api/pith-number/TAVIL74N3QQX3WLNWVSHP5M2NP/graph.json","events_json":"https://pith.science/api/pith-number/TAVIL74N3QQX3WLNWVSHP5M2NP/events.json","paper":"https://pith.science/paper/TAVIL74N"},"agent_actions":{"view_html":"https://pith.science/pith/TAVIL74N3QQX3WLNWVSHP5M2NP","download_json":"https://pith.science/pith/TAVIL74N3QQX3WLNWVSHP5M2NP.json","view_paper":"https://pith.science/paper/TAVIL74N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.06212&json=true","fetch_graph":"https://pith.science/api/pith-number/TAVIL74N3QQX3WLNWVSHP5M2NP/graph.json","fetch_events":"https://pith.science/api/pith-number/TAVIL74N3QQX3WLNWVSHP5M2NP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TAVIL74N3QQX3WLNWVSHP5M2NP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TAVIL74N3QQX3WLNWVSHP5M2NP/action/storage_attestation","attest_author":"https://pith.science/pith/TAVIL74N3QQX3WLNWVSHP5M2NP/action/author_attestation","sign_citation":"https://pith.science/pith/TAVIL74N3QQX3WLNWVSHP5M2NP/action/citation_signature","submit_replication":"https://pith.science/pith/TAVIL74N3QQX3WLNWVSHP5M2NP/action/replication_record"}},"created_at":"2026-07-05T09:46:17.244326+00:00","updated_at":"2026-07-05T09:46:17.244326+00:00"}