{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:56K52TIRGKRNGCFWNCAPEFCW4N","short_pith_number":"pith:56K52TIR","schema_version":"1.0","canonical_sha256":"ef95dd4d1132a2d308b66880f21456e34bce8a09347717f8d9bfd58b2839b5e2","source":{"kind":"arxiv","id":"2305.10668","version":2},"attestation_state":"computed","paper":{"title":"MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR","cs.SI"],"primary_cat":"cs.LG","authors_text":"Canyu Chen, Kai Shu, Kaize Ding, Xiongxiao Xu","submitted_at":"2023-05-18T03:04:51Z","abstract_excerpt":"Graph anomaly detection has long been an important problem in various domains pertaining to information security such as financial fraud, social spam and network intrusion. The majority of existing methods are performed in an unsupervised manner, as labeled anomalies in a large scale are often too expensive to acquire. However, the identified anomalies may turn out to be uninteresting data instances due to the lack of prior knowledge. In real-world scenarios, it is often feasible to obtain limited labeled anomalies, which have great potential to advance graph anomaly detection. However, the wo"},"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":"2305.10668","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-18T03:04:51Z","cross_cats_sorted":["cs.AI","cs.CR","cs.SI"],"title_canon_sha256":"fdc0830e10f725a25f0b1c424c56d7083cdc1aa5454ca2ca033830a486237e14","abstract_canon_sha256":"ea32952d64ec5b37a9b4069ff6d7bc3884f04bfe09e2a7559f3a6b5ef7098799"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:58:39.846677Z","signature_b64":"jBPNrLLw+ZAl3Vfexm3s9oBsBkA64vfr7AP5SG8Iv20/kXA6X0xVO7ZzG0NmQc2krruY9VYY4SVqskS4YWitAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef95dd4d1132a2d308b66880f21456e34bce8a09347717f8d9bfd58b2839b5e2","last_reissued_at":"2026-07-05T08:58:39.846192Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:58:39.846192Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR","cs.SI"],"primary_cat":"cs.LG","authors_text":"Canyu Chen, Kai Shu, Kaize Ding, Xiongxiao Xu","submitted_at":"2023-05-18T03:04:51Z","abstract_excerpt":"Graph anomaly detection has long been an important problem in various domains pertaining to information security such as financial fraud, social spam and network intrusion. The majority of existing methods are performed in an unsupervised manner, as labeled anomalies in a large scale are often too expensive to acquire. However, the identified anomalies may turn out to be uninteresting data instances due to the lack of prior knowledge. In real-world scenarios, it is often feasible to obtain limited labeled anomalies, which have great potential to advance graph anomaly detection. However, the wo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.10668","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/2305.10668/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":"2305.10668","created_at":"2026-07-05T08:58:39.846275+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.10668v2","created_at":"2026-07-05T08:58:39.846275+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.10668","created_at":"2026-07-05T08:58:39.846275+00:00"},{"alias_kind":"pith_short_12","alias_value":"56K52TIRGKRN","created_at":"2026-07-05T08:58:39.846275+00:00"},{"alias_kind":"pith_short_16","alias_value":"56K52TIRGKRNGCFW","created_at":"2026-07-05T08:58:39.846275+00:00"},{"alias_kind":"pith_short_8","alias_value":"56K52TIR","created_at":"2026-07-05T08:58:39.846275+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.04190","citing_title":"How to Use Graph Data in the Wild to Help Graph Anomaly Detection?","ref_index":52,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/56K52TIRGKRNGCFWNCAPEFCW4N","json":"https://pith.science/pith/56K52TIRGKRNGCFWNCAPEFCW4N.json","graph_json":"https://pith.science/api/pith-number/56K52TIRGKRNGCFWNCAPEFCW4N/graph.json","events_json":"https://pith.science/api/pith-number/56K52TIRGKRNGCFWNCAPEFCW4N/events.json","paper":"https://pith.science/paper/56K52TIR"},"agent_actions":{"view_html":"https://pith.science/pith/56K52TIRGKRNGCFWNCAPEFCW4N","download_json":"https://pith.science/pith/56K52TIRGKRNGCFWNCAPEFCW4N.json","view_paper":"https://pith.science/paper/56K52TIR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.10668&json=true","fetch_graph":"https://pith.science/api/pith-number/56K52TIRGKRNGCFWNCAPEFCW4N/graph.json","fetch_events":"https://pith.science/api/pith-number/56K52TIRGKRNGCFWNCAPEFCW4N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/56K52TIRGKRNGCFWNCAPEFCW4N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/56K52TIRGKRNGCFWNCAPEFCW4N/action/storage_attestation","attest_author":"https://pith.science/pith/56K52TIRGKRNGCFWNCAPEFCW4N/action/author_attestation","sign_citation":"https://pith.science/pith/56K52TIRGKRNGCFWNCAPEFCW4N/action/citation_signature","submit_replication":"https://pith.science/pith/56K52TIRGKRNGCFWNCAPEFCW4N/action/replication_record"}},"created_at":"2026-07-05T08:58:39.846275+00:00","updated_at":"2026-07-05T08:58:39.846275+00:00"}