{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VRVEUFS47TNKO7MFQIOJNIBGXI","short_pith_number":"pith:VRVEUFS4","schema_version":"1.0","canonical_sha256":"ac6a4a165cfcdaa77d85821c96a026ba231f4a437491b1dc418a62cea187a9a5","source":{"kind":"arxiv","id":"2412.05957","version":1},"attestation_state":"computed","paper":{"title":"A Two-Stage AI-Powered Motif Mining Method for Efficient Power System Topological Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Jian Ping, Jianzhong Wu, Xiaoyuan Xu, Yiyan Li, Zhenghao Zhou, Zheng Yan","submitted_at":"2024-12-08T14:30:21Z","abstract_excerpt":"Graph motif, defined as the microstructure that appears repeatedly in a large graph, reveals important topological characteristics of the large graph and has gained increasing attention in power system analysis regarding reliability, vulnerability and resiliency. However, searching motifs within the large-scale power system is extremely computationally challenging and even infeasible, which undermines the value of motif analysis in practice. In this paper, we introduce a two-stage AI-powered motif mining method to enable efficient and wide-range motif analysis in power systems. In the first st"},"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":"2412.05957","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2024-12-08T14:30:21Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"550a4d55765f8ee1ae41e808fc9fed29c49ef2c13b9201c4a3d36c3a2e6981ba","abstract_canon_sha256":"3e972c5b233f4722c16eaaf6ac575d1b9a6d86939116d631cdffc811ad0326f8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:46:12.170191Z","signature_b64":"1YtMA38c0MGwf9JtoRe2hNaoEaxDQyIRj3JiC5b5JzE1UQcxcoWclvZySQd+j31r/cRUGSWvwZB6dG26Fk/9Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac6a4a165cfcdaa77d85821c96a026ba231f4a437491b1dc418a62cea187a9a5","last_reissued_at":"2026-07-05T09:46:12.169805Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:46:12.169805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Two-Stage AI-Powered Motif Mining Method for Efficient Power System Topological Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Jian Ping, Jianzhong Wu, Xiaoyuan Xu, Yiyan Li, Zhenghao Zhou, Zheng Yan","submitted_at":"2024-12-08T14:30:21Z","abstract_excerpt":"Graph motif, defined as the microstructure that appears repeatedly in a large graph, reveals important topological characteristics of the large graph and has gained increasing attention in power system analysis regarding reliability, vulnerability and resiliency. However, searching motifs within the large-scale power system is extremely computationally challenging and even infeasible, which undermines the value of motif analysis in practice. In this paper, we introduce a two-stage AI-powered motif mining method to enable efficient and wide-range motif analysis in power systems. In the first st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.05957","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/2412.05957/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":"2412.05957","created_at":"2026-07-05T09:46:12.169872+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.05957v1","created_at":"2026-07-05T09:46:12.169872+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.05957","created_at":"2026-07-05T09:46:12.169872+00:00"},{"alias_kind":"pith_short_12","alias_value":"VRVEUFS47TNK","created_at":"2026-07-05T09:46:12.169872+00:00"},{"alias_kind":"pith_short_16","alias_value":"VRVEUFS47TNKO7MF","created_at":"2026-07-05T09:46:12.169872+00:00"},{"alias_kind":"pith_short_8","alias_value":"VRVEUFS4","created_at":"2026-07-05T09:46:12.169872+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/VRVEUFS47TNKO7MFQIOJNIBGXI","json":"https://pith.science/pith/VRVEUFS47TNKO7MFQIOJNIBGXI.json","graph_json":"https://pith.science/api/pith-number/VRVEUFS47TNKO7MFQIOJNIBGXI/graph.json","events_json":"https://pith.science/api/pith-number/VRVEUFS47TNKO7MFQIOJNIBGXI/events.json","paper":"https://pith.science/paper/VRVEUFS4"},"agent_actions":{"view_html":"https://pith.science/pith/VRVEUFS47TNKO7MFQIOJNIBGXI","download_json":"https://pith.science/pith/VRVEUFS47TNKO7MFQIOJNIBGXI.json","view_paper":"https://pith.science/paper/VRVEUFS4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.05957&json=true","fetch_graph":"https://pith.science/api/pith-number/VRVEUFS47TNKO7MFQIOJNIBGXI/graph.json","fetch_events":"https://pith.science/api/pith-number/VRVEUFS47TNKO7MFQIOJNIBGXI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VRVEUFS47TNKO7MFQIOJNIBGXI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VRVEUFS47TNKO7MFQIOJNIBGXI/action/storage_attestation","attest_author":"https://pith.science/pith/VRVEUFS47TNKO7MFQIOJNIBGXI/action/author_attestation","sign_citation":"https://pith.science/pith/VRVEUFS47TNKO7MFQIOJNIBGXI/action/citation_signature","submit_replication":"https://pith.science/pith/VRVEUFS47TNKO7MFQIOJNIBGXI/action/replication_record"}},"created_at":"2026-07-05T09:46:12.169872+00:00","updated_at":"2026-07-05T09:46:12.169872+00:00"}