{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:WNIGYTZRI6DXAFPSUIWCL2OWAM","short_pith_number":"pith:WNIGYTZR","canonical_record":{"source":{"id":"2405.07626","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-13T10:37:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"34a6e2fe7f9a257e86d6daee4bf1ac1c817df793bc7a171b8629b9e298dba785","abstract_canon_sha256":"2a35a663c8ffb6fe514e49c9bb9b8e83b51c5555a339b4b76eb1c8c2cff4c4b0"},"schema_version":"1.0"},"canonical_sha256":"b3506c4f3147877015f2a22c25e9d6031184339b1de54431b0c1ccdecc3597f9","source":{"kind":"arxiv","id":"2405.07626","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.07626","created_at":"2026-07-05T09:00:04Z"},{"alias_kind":"arxiv_version","alias_value":"2405.07626v2","created_at":"2026-07-05T09:00:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.07626","created_at":"2026-07-05T09:00:04Z"},{"alias_kind":"pith_short_12","alias_value":"WNIGYTZRI6DX","created_at":"2026-07-05T09:00:04Z"},{"alias_kind":"pith_short_16","alias_value":"WNIGYTZRI6DXAFPS","created_at":"2026-07-05T09:00:04Z"},{"alias_kind":"pith_short_8","alias_value":"WNIGYTZR","created_at":"2026-07-05T09:00:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:WNIGYTZRI6DXAFPSUIWCL2OWAM","target":"record","payload":{"canonical_record":{"source":{"id":"2405.07626","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-13T10:37:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"34a6e2fe7f9a257e86d6daee4bf1ac1c817df793bc7a171b8629b9e298dba785","abstract_canon_sha256":"2a35a663c8ffb6fe514e49c9bb9b8e83b51c5555a339b4b76eb1c8c2cff4c4b0"},"schema_version":"1.0"},"canonical_sha256":"b3506c4f3147877015f2a22c25e9d6031184339b1de54431b0c1ccdecc3597f9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:00:04.043886Z","signature_b64":"+RdC3Fm8+xkvHHmtt3uGnVaNtLGXPtp280iSSikw24YR4Ng+Hwp0ZhrvEG86Obku8JbSSxBIjqH+x9pj4F0pDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3506c4f3147877015f2a22c25e9d6031184339b1de54431b0c1ccdecc3597f9","last_reissued_at":"2026-07-05T09:00:04.043380Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:00:04.043380Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.07626","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:00:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N1v7auZbAlWKLeYMSZ41IWCWjLjUieVFDAihb/GjTgdA9NflnKCOqflmwFQN7xofNTwPVuPfHpYvpOSMjnFBAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T22:53:13.304445Z"},"content_sha256":"8e96e5258f65ff19c5fb1dc526cae5e50c18f9fc8d53d13e6099fffb7c4e85fd","schema_version":"1.0","event_id":"sha256:8e96e5258f65ff19c5fb1dc526cae5e50c18f9fc8d53d13e6099fffb7c4e85fd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:WNIGYTZRI6DXAFPSUIWCL2OWAM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Di Yao, Jingping Bi, Kaiyu Feng, Lanting Fang, Shuo Liu, Wenbin Li, XiaoWen Ji, Zhetao Li","submitted_at":"2024-05-13T10:37:50Z","abstract_excerpt":"Detecting anomaly edges for dynamic graphs aims to identify edges significantly deviating from the normal pattern and can be applied in various domains, such as cybersecurity, financial transactions and AIOps. With the evolving of time, the types of anomaly edges are emerging and the labeled anomaly samples are few for each type. Current methods are either designed to detect randomly inserted edges or require sufficient labeled data for model training, which harms their applicability for real-world applications. In this paper, we study this problem by cooperating with the rich knowledge encode"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.07626","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/2405.07626/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:00:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h/SSYdjZ8FVAUAT7EGVzmjmL4ZfjQfkbFSHpj7/9v+UX2VkFgMUrDrCcFZ6T6bX4uvurXF+W3klD7PY5Z2YHDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T22:53:13.304962Z"},"content_sha256":"9e0730a0d5ba3c30ac36c5cadb8fa80eed489c4879de5a23a83b7ec7ec3203da","schema_version":"1.0","event_id":"sha256:9e0730a0d5ba3c30ac36c5cadb8fa80eed489c4879de5a23a83b7ec7ec3203da"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WNIGYTZRI6DXAFPSUIWCL2OWAM/bundle.json","state_url":"https://pith.science/pith/WNIGYTZRI6DXAFPSUIWCL2OWAM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WNIGYTZRI6DXAFPSUIWCL2OWAM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T22:53:13Z","links":{"resolver":"https://pith.science/pith/WNIGYTZRI6DXAFPSUIWCL2OWAM","bundle":"https://pith.science/pith/WNIGYTZRI6DXAFPSUIWCL2OWAM/bundle.json","state":"https://pith.science/pith/WNIGYTZRI6DXAFPSUIWCL2OWAM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WNIGYTZRI6DXAFPSUIWCL2OWAM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:WNIGYTZRI6DXAFPSUIWCL2OWAM","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":"2a35a663c8ffb6fe514e49c9bb9b8e83b51c5555a339b4b76eb1c8c2cff4c4b0","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-13T10:37:50Z","title_canon_sha256":"34a6e2fe7f9a257e86d6daee4bf1ac1c817df793bc7a171b8629b9e298dba785"},"schema_version":"1.0","source":{"id":"2405.07626","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.07626","created_at":"2026-07-05T09:00:04Z"},{"alias_kind":"arxiv_version","alias_value":"2405.07626v2","created_at":"2026-07-05T09:00:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.07626","created_at":"2026-07-05T09:00:04Z"},{"alias_kind":"pith_short_12","alias_value":"WNIGYTZRI6DX","created_at":"2026-07-05T09:00:04Z"},{"alias_kind":"pith_short_16","alias_value":"WNIGYTZRI6DXAFPS","created_at":"2026-07-05T09:00:04Z"},{"alias_kind":"pith_short_8","alias_value":"WNIGYTZR","created_at":"2026-07-05T09:00:04Z"}],"graph_snapshots":[{"event_id":"sha256:9e0730a0d5ba3c30ac36c5cadb8fa80eed489c4879de5a23a83b7ec7ec3203da","target":"graph","created_at":"2026-07-05T09:00:04Z","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/2405.07626/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Detecting anomaly edges for dynamic graphs aims to identify edges significantly deviating from the normal pattern and can be applied in various domains, such as cybersecurity, financial transactions and AIOps. With the evolving of time, the types of anomaly edges are emerging and the labeled anomaly samples are few for each type. Current methods are either designed to detect randomly inserted edges or require sufficient labeled data for model training, which harms their applicability for real-world applications. In this paper, we study this problem by cooperating with the rich knowledge encode","authors_text":"Di Yao, Jingping Bi, Kaiyu Feng, Lanting Fang, Shuo Liu, Wenbin Li, XiaoWen Ji, Zhetao Li","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-13T10:37:50Z","title":"AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.07626","kind":"arxiv","version":2},"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":"sha256:8e96e5258f65ff19c5fb1dc526cae5e50c18f9fc8d53d13e6099fffb7c4e85fd","target":"record","created_at":"2026-07-05T09:00:04Z","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":"2a35a663c8ffb6fe514e49c9bb9b8e83b51c5555a339b4b76eb1c8c2cff4c4b0","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-13T10:37:50Z","title_canon_sha256":"34a6e2fe7f9a257e86d6daee4bf1ac1c817df793bc7a171b8629b9e298dba785"},"schema_version":"1.0","source":{"id":"2405.07626","kind":"arxiv","version":2}},"canonical_sha256":"b3506c4f3147877015f2a22c25e9d6031184339b1de54431b0c1ccdecc3597f9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b3506c4f3147877015f2a22c25e9d6031184339b1de54431b0c1ccdecc3597f9","first_computed_at":"2026-07-05T09:00:04.043380Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:00:04.043380Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+RdC3Fm8+xkvHHmtt3uGnVaNtLGXPtp280iSSikw24YR4Ng+Hwp0ZhrvEG86Obku8JbSSxBIjqH+x9pj4F0pDw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:00:04.043886Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.07626","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8e96e5258f65ff19c5fb1dc526cae5e50c18f9fc8d53d13e6099fffb7c4e85fd","sha256:9e0730a0d5ba3c30ac36c5cadb8fa80eed489c4879de5a23a83b7ec7ec3203da"],"state_sha256":"a0fad4cc3d10c8c32f4689cbc85f40923d2cf7ef25d42121ca0e6cc5448e1367"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AW2rl4wzjI/WZN3MSB3kd0fK4UGR/Ml3jU5vlf4R/j/PgiAswvaltmLvN6bgRJIxe/KFHm93H/0d9teKg9ZjBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T22:53:13.309191Z","bundle_sha256":"39c51f7ce6c037d741d141ff1063ed6b2e9e925fdfe286b549cb3e3f990c9822"}}