{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OWNZ44MC6ED6RIAJRWNHW6OXQU","short_pith_number":"pith:OWNZ44MC","schema_version":"1.0","canonical_sha256":"759b9e7182f107e8a0098d9a7b79d7853e86b1336854547971222a89d7c85c9b","source":{"kind":"arxiv","id":"2504.14205","version":2},"attestation_state":"computed","paper":{"title":"Dual-channel Heterophilic Message Passing for Graph Fraud Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Cuicui Luo, Guangzhen Yao, Jingxing Zhong, Renda Han, Wenxin Zhang, Xiaojian Lin, Zeyu Zhang","submitted_at":"2025-04-19T06:41:24Z","abstract_excerpt":"Fraudulent activities have significantly increased across various domains, such as e-commerce, online review platforms, and social networks, making fraud detection a critical task. Spatial Graph Neural Networks (GNNs) have been successfully applied to fraud detection tasks due to their strong inductive learning capabilities. However, existing spatial GNN-based methods often enhance the graph structure by excluding heterophilic neighbors during message passing to align with the homophilic bias of GNNs. Unfortunately, this approach can disrupt the original graph topology and increase uncertainty"},"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":"2504.14205","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-19T06:41:24Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5275493bee8539d70ab65005793474ad3965dd8347b267a5fab5cb84717c4f70","abstract_canon_sha256":"b0304c9b1d6ed4db4d689865aa5874dfbd5547a17d23367fcc6cf73e8480fa64"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:54:16.440279Z","signature_b64":"SpNpkUQhaJ/d+Ckjtt7tsEWUYujZN+nNh4bnGcAY+V3L+3h79YXNWoWS1IztTB9b7LzR9LrDg6cAM8wb8kZvAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"759b9e7182f107e8a0098d9a7b79d7853e86b1336854547971222a89d7c85c9b","last_reissued_at":"2026-07-05T10:54:16.439728Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:54:16.439728Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dual-channel Heterophilic Message Passing for Graph Fraud Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Cuicui Luo, Guangzhen Yao, Jingxing Zhong, Renda Han, Wenxin Zhang, Xiaojian Lin, Zeyu Zhang","submitted_at":"2025-04-19T06:41:24Z","abstract_excerpt":"Fraudulent activities have significantly increased across various domains, such as e-commerce, online review platforms, and social networks, making fraud detection a critical task. Spatial Graph Neural Networks (GNNs) have been successfully applied to fraud detection tasks due to their strong inductive learning capabilities. However, existing spatial GNN-based methods often enhance the graph structure by excluding heterophilic neighbors during message passing to align with the homophilic bias of GNNs. Unfortunately, this approach can disrupt the original graph topology and increase uncertainty"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.14205","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/2504.14205/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":"2504.14205","created_at":"2026-07-05T10:54:16.439786+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.14205v2","created_at":"2026-07-05T10:54:16.439786+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.14205","created_at":"2026-07-05T10:54:16.439786+00:00"},{"alias_kind":"pith_short_12","alias_value":"OWNZ44MC6ED6","created_at":"2026-07-05T10:54:16.439786+00:00"},{"alias_kind":"pith_short_16","alias_value":"OWNZ44MC6ED6RIAJ","created_at":"2026-07-05T10:54:16.439786+00:00"},{"alias_kind":"pith_short_8","alias_value":"OWNZ44MC","created_at":"2026-07-05T10:54:16.439786+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.01161","citing_title":"Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers","ref_index":64,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OWNZ44MC6ED6RIAJRWNHW6OXQU","json":"https://pith.science/pith/OWNZ44MC6ED6RIAJRWNHW6OXQU.json","graph_json":"https://pith.science/api/pith-number/OWNZ44MC6ED6RIAJRWNHW6OXQU/graph.json","events_json":"https://pith.science/api/pith-number/OWNZ44MC6ED6RIAJRWNHW6OXQU/events.json","paper":"https://pith.science/paper/OWNZ44MC"},"agent_actions":{"view_html":"https://pith.science/pith/OWNZ44MC6ED6RIAJRWNHW6OXQU","download_json":"https://pith.science/pith/OWNZ44MC6ED6RIAJRWNHW6OXQU.json","view_paper":"https://pith.science/paper/OWNZ44MC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.14205&json=true","fetch_graph":"https://pith.science/api/pith-number/OWNZ44MC6ED6RIAJRWNHW6OXQU/graph.json","fetch_events":"https://pith.science/api/pith-number/OWNZ44MC6ED6RIAJRWNHW6OXQU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OWNZ44MC6ED6RIAJRWNHW6OXQU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OWNZ44MC6ED6RIAJRWNHW6OXQU/action/storage_attestation","attest_author":"https://pith.science/pith/OWNZ44MC6ED6RIAJRWNHW6OXQU/action/author_attestation","sign_citation":"https://pith.science/pith/OWNZ44MC6ED6RIAJRWNHW6OXQU/action/citation_signature","submit_replication":"https://pith.science/pith/OWNZ44MC6ED6RIAJRWNHW6OXQU/action/replication_record"}},"created_at":"2026-07-05T10:54:16.439786+00:00","updated_at":"2026-07-05T10:54:16.439786+00:00"}