{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:C3B34U7IIJW35LWCF7VPTWJU3Y","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3fddc0d118b89275578a55314f0f8a0a3f28e72d87f3c2d577e30b91a475499d","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.ST","submitted_at":"2024-10-29T06:11:36Z","title_canon_sha256":"ea714fae469812bd35828bf1e18b27e2dd57deed5dab3158cd3114b720e93627"},"schema_version":"1.0","source":{"id":"2411.11848","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.11848","created_at":"2026-07-05T09:37:12Z"},{"alias_kind":"arxiv_version","alias_value":"2411.11848v1","created_at":"2026-07-05T09:37:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.11848","created_at":"2026-07-05T09:37:12Z"},{"alias_kind":"pith_short_12","alias_value":"C3B34U7IIJW3","created_at":"2026-07-05T09:37:12Z"},{"alias_kind":"pith_short_16","alias_value":"C3B34U7IIJW35LWC","created_at":"2026-07-05T09:37:12Z"},{"alias_kind":"pith_short_8","alias_value":"C3B34U7I","created_at":"2026-07-05T09:37:12Z"}],"graph_snapshots":[{"event_id":"sha256:a53918ba134f32e4e94b0c43f86bd28761d983ce5801f07a3e054dc78a9aecd9","target":"graph","created_at":"2026-07-05T09:37:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2411.11848/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the current context of accelerated globalization and digitalization, the complexity and uncertainty of financial markets are increasing, and the identification and prevention of economic risks have become a key link in maintaining the stability of the financial system. Traditional risk identification methods often have limitations because they are difficult to cope with the multi-level and dynamically changing complex relationships in financial networks. With the rapid development of financial technology, graph neural network (GNN) technology, as an emerging deep learning method, has gradua","authors_text":"Mengfang Sun, Tong Zhou, Wenying Sun, Xin Zhang, Yue Liu, Zhen Xu","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.ST","submitted_at":"2024-10-29T06:11:36Z","title":"Robust Graph Neural Networks for Stability Analysis in Dynamic Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.11848","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b737756d2b30b9ac2d1f106c9f46b10c839f789de87dba33536fe80ec8f21e4e","target":"record","created_at":"2026-07-05T09:37:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3fddc0d118b89275578a55314f0f8a0a3f28e72d87f3c2d577e30b91a475499d","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.ST","submitted_at":"2024-10-29T06:11:36Z","title_canon_sha256":"ea714fae469812bd35828bf1e18b27e2dd57deed5dab3158cd3114b720e93627"},"schema_version":"1.0","source":{"id":"2411.11848","kind":"arxiv","version":1}},"canonical_sha256":"16c3be53e8426dbeaec22feaf9d934de1ceddc5fc7f0cf9b90f972ce62953076","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"16c3be53e8426dbeaec22feaf9d934de1ceddc5fc7f0cf9b90f972ce62953076","first_computed_at":"2026-07-05T09:37:12.512083Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:37:12.512083Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wjd+zYCTAbKrLOT7+qucbroU7wq8dEXf11Bk4T3fbgvlPlx0RetJ5fa8PTM5jIz2H75J1a65jOV3W616y+a8Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:37:12.512566Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.11848","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b737756d2b30b9ac2d1f106c9f46b10c839f789de87dba33536fe80ec8f21e4e","sha256:a53918ba134f32e4e94b0c43f86bd28761d983ce5801f07a3e054dc78a9aecd9"],"state_sha256":"5315c33a0f75c4381722dbeed2a5610ad10eafd1680775b0f2438c4646e65f30"}