{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:S6PLUJKKXTN4ZRDPX7ZDP736EE","short_pith_number":"pith:S6PLUJKK","schema_version":"1.0","canonical_sha256":"979eba254abcdbccc46fbff237ff7e2100a0dbc7f612401e8ae0b9d0d8872821","source":{"kind":"arxiv","id":"2509.10869","version":1},"attestation_state":"computed","paper":{"title":"GTHNA: Local-global Graph Transformer with Memory Reconstruction for Holistic Node Anomaly Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Fu Lin, Mingkang Li, Xuexiong Luo, Yaoyang Li, Yue Zhang","submitted_at":"2025-09-13T15:52:16Z","abstract_excerpt":"Anomaly detection in graph-structured data is an inherently challenging problem, as it requires the identification of rare nodes that deviate from the majority in both their structural and behavioral characteristics. Existing methods, such as those based on graph convolutional networks (GCNs), often suffer from over-smoothing, which causes the learned node representations to become indistinguishable. Furthermore, graph reconstruction-based approaches are vulnerable to anomalous node interference during the reconstruction process, leading to inaccurate anomaly detection. In this work, we propos"},"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":"2509.10869","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-13T15:52:16Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8f2319fd3dedfee4dddf617f86777623d1e413b5456240944d3b39644114db54","abstract_canon_sha256":"129424045e4e52d16f199f5c2e85690ed0b361d8fced0b0f0546223aceab7d35"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:11:29.504616Z","signature_b64":"/H1tYgUQ2gpYmpJrP0yhwr41l1w8J0luemKO0CpEerIBZOiZCNmJdgnGgQZwNzozbBg31Lmh9S7o+dPW5GbSCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"979eba254abcdbccc46fbff237ff7e2100a0dbc7f612401e8ae0b9d0d8872821","last_reissued_at":"2026-07-05T12:11:29.504141Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:11:29.504141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GTHNA: Local-global Graph Transformer with Memory Reconstruction for Holistic Node Anomaly Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Fu Lin, Mingkang Li, Xuexiong Luo, Yaoyang Li, Yue Zhang","submitted_at":"2025-09-13T15:52:16Z","abstract_excerpt":"Anomaly detection in graph-structured data is an inherently challenging problem, as it requires the identification of rare nodes that deviate from the majority in both their structural and behavioral characteristics. Existing methods, such as those based on graph convolutional networks (GCNs), often suffer from over-smoothing, which causes the learned node representations to become indistinguishable. Furthermore, graph reconstruction-based approaches are vulnerable to anomalous node interference during the reconstruction process, leading to inaccurate anomaly detection. In this work, we propos"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.10869","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/2509.10869/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":"2509.10869","created_at":"2026-07-05T12:11:29.504198+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.10869v1","created_at":"2026-07-05T12:11:29.504198+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.10869","created_at":"2026-07-05T12:11:29.504198+00:00"},{"alias_kind":"pith_short_12","alias_value":"S6PLUJKKXTN4","created_at":"2026-07-05T12:11:29.504198+00:00"},{"alias_kind":"pith_short_16","alias_value":"S6PLUJKKXTN4ZRDP","created_at":"2026-07-05T12:11:29.504198+00:00"},{"alias_kind":"pith_short_8","alias_value":"S6PLUJKK","created_at":"2026-07-05T12:11:29.504198+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/S6PLUJKKXTN4ZRDPX7ZDP736EE","json":"https://pith.science/pith/S6PLUJKKXTN4ZRDPX7ZDP736EE.json","graph_json":"https://pith.science/api/pith-number/S6PLUJKKXTN4ZRDPX7ZDP736EE/graph.json","events_json":"https://pith.science/api/pith-number/S6PLUJKKXTN4ZRDPX7ZDP736EE/events.json","paper":"https://pith.science/paper/S6PLUJKK"},"agent_actions":{"view_html":"https://pith.science/pith/S6PLUJKKXTN4ZRDPX7ZDP736EE","download_json":"https://pith.science/pith/S6PLUJKKXTN4ZRDPX7ZDP736EE.json","view_paper":"https://pith.science/paper/S6PLUJKK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.10869&json=true","fetch_graph":"https://pith.science/api/pith-number/S6PLUJKKXTN4ZRDPX7ZDP736EE/graph.json","fetch_events":"https://pith.science/api/pith-number/S6PLUJKKXTN4ZRDPX7ZDP736EE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S6PLUJKKXTN4ZRDPX7ZDP736EE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S6PLUJKKXTN4ZRDPX7ZDP736EE/action/storage_attestation","attest_author":"https://pith.science/pith/S6PLUJKKXTN4ZRDPX7ZDP736EE/action/author_attestation","sign_citation":"https://pith.science/pith/S6PLUJKKXTN4ZRDPX7ZDP736EE/action/citation_signature","submit_replication":"https://pith.science/pith/S6PLUJKKXTN4ZRDPX7ZDP736EE/action/replication_record"}},"created_at":"2026-07-05T12:11:29.504198+00:00","updated_at":"2026-07-05T12:11:29.504198+00:00"}