{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:JG4A3YQDMFPVGYSUE3H5RSCEIN","short_pith_number":"pith:JG4A3YQD","canonical_record":{"source":{"id":"1809.04683","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-12T21:28:26Z","cross_cats_sorted":["cs.AI","cs.CR","stat.ML"],"title_canon_sha256":"5699c0f920aa5d9af50bf444cd356d760a14923204ff9ced5128a3e6184ee595","abstract_canon_sha256":"302f2fb30e7544ba3dd350c4486001fadbdaa379dc9990ad4c18e6b460c9ca48"},"schema_version":"1.0"},"canonical_sha256":"49b80de203615f53625426cfd8c8444368edcb7a1419536b7bfa6a25ca54505b","source":{"kind":"arxiv","id":"1809.04683","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.04683","created_at":"2026-05-18T00:00:43Z"},{"alias_kind":"arxiv_version","alias_value":"1809.04683v2","created_at":"2026-05-18T00:00:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.04683","created_at":"2026-05-18T00:00:43Z"},{"alias_kind":"pith_short_12","alias_value":"JG4A3YQDMFPV","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"JG4A3YQDMFPVGYSU","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"JG4A3YQD","created_at":"2026-05-18T12:32:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:JG4A3YQDMFPVGYSUE3H5RSCEIN","target":"record","payload":{"canonical_record":{"source":{"id":"1809.04683","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-12T21:28:26Z","cross_cats_sorted":["cs.AI","cs.CR","stat.ML"],"title_canon_sha256":"5699c0f920aa5d9af50bf444cd356d760a14923204ff9ced5128a3e6184ee595","abstract_canon_sha256":"302f2fb30e7544ba3dd350c4486001fadbdaa379dc9990ad4c18e6b460c9ca48"},"schema_version":"1.0"},"canonical_sha256":"49b80de203615f53625426cfd8c8444368edcb7a1419536b7bfa6a25ca54505b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:43.048774Z","signature_b64":"WR6FJzMrN4KeQUSdm4fpk5mH2B0RGfbfA85i/1zfj2y+SCroJxnvKlm1hgBL4ybjHlWmzzsCkBuXYb3Nga9rAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49b80de203615f53625426cfd8c8444368edcb7a1419536b7bfa6a25ca54505b","last_reissued_at":"2026-05-18T00:00:43.048383Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:43.048383Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.04683","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":"2026-05-18T00:00:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wzo4v6/Sqh+ydIv3Di5bKR0GHJieYOlVEim8lqehOOcv9gV8mKV0KfwGRzjkXn650fNVdi7SUbnEvEvVN08fAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T21:49:17.856101Z"},"content_sha256":"dce9ee90a33f644b2bc454a6f65a4fb1e7196729d5e38197c4d7729c9f0fa5bf","schema_version":"1.0","event_id":"sha256:dce9ee90a33f644b2bc454a6f65a4fb1e7196729d5e38197c4d7729c9f0fa5bf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:JG4A3YQDMFPVGYSUE3H5RSCEIN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SAFE: A Neural Survival Analysis Model for Fraud Early Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CR","stat.ML"],"primary_cat":"cs.LG","authors_text":"Panpan Zheng, Shuhan Yuan, Xintao Wu","submitted_at":"2018-09-12T21:28:26Z","abstract_excerpt":"Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the existing approaches adopt classifiers to predict fraudsters given their activity sequences along time. The main drawback of classification models is that the prediction results between consecutive timestamps are often inconsistent. In this paper, we propose a survival analysis based"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.04683","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":""},"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":"2026-05-18T00:00:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qnB5a/bFJmO1ZEQ/ari6zq2Lz54fk1Gj4Dj4XXqsuSi9kw0a27kMUjavvBDydFZFRsNsMNbk5mp1Knc5BtkkDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T21:49:17.856469Z"},"content_sha256":"1dd7563105cf3427a6bcfad871b1660713415508a6b2eb9a5ab3972a1ed87394","schema_version":"1.0","event_id":"sha256:1dd7563105cf3427a6bcfad871b1660713415508a6b2eb9a5ab3972a1ed87394"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JG4A3YQDMFPVGYSUE3H5RSCEIN/bundle.json","state_url":"https://pith.science/pith/JG4A3YQDMFPVGYSUE3H5RSCEIN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JG4A3YQDMFPVGYSUE3H5RSCEIN/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":"2026-06-03T21:49:17Z","links":{"resolver":"https://pith.science/pith/JG4A3YQDMFPVGYSUE3H5RSCEIN","bundle":"https://pith.science/pith/JG4A3YQDMFPVGYSUE3H5RSCEIN/bundle.json","state":"https://pith.science/pith/JG4A3YQDMFPVGYSUE3H5RSCEIN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JG4A3YQDMFPVGYSUE3H5RSCEIN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:JG4A3YQDMFPVGYSUE3H5RSCEIN","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":"302f2fb30e7544ba3dd350c4486001fadbdaa379dc9990ad4c18e6b460c9ca48","cross_cats_sorted":["cs.AI","cs.CR","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-12T21:28:26Z","title_canon_sha256":"5699c0f920aa5d9af50bf444cd356d760a14923204ff9ced5128a3e6184ee595"},"schema_version":"1.0","source":{"id":"1809.04683","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.04683","created_at":"2026-05-18T00:00:43Z"},{"alias_kind":"arxiv_version","alias_value":"1809.04683v2","created_at":"2026-05-18T00:00:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.04683","created_at":"2026-05-18T00:00:43Z"},{"alias_kind":"pith_short_12","alias_value":"JG4A3YQDMFPV","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"JG4A3YQDMFPVGYSU","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"JG4A3YQD","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:1dd7563105cf3427a6bcfad871b1660713415508a6b2eb9a5ab3972a1ed87394","target":"graph","created_at":"2026-05-18T00:00:43Z","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"},"paper":{"abstract_excerpt":"Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the existing approaches adopt classifiers to predict fraudsters given their activity sequences along time. The main drawback of classification models is that the prediction results between consecutive timestamps are often inconsistent. In this paper, we propose a survival analysis based","authors_text":"Panpan Zheng, Shuhan Yuan, Xintao Wu","cross_cats":["cs.AI","cs.CR","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-12T21:28:26Z","title":"SAFE: A Neural Survival Analysis Model for Fraud Early Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.04683","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:dce9ee90a33f644b2bc454a6f65a4fb1e7196729d5e38197c4d7729c9f0fa5bf","target":"record","created_at":"2026-05-18T00:00:43Z","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":"302f2fb30e7544ba3dd350c4486001fadbdaa379dc9990ad4c18e6b460c9ca48","cross_cats_sorted":["cs.AI","cs.CR","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-12T21:28:26Z","title_canon_sha256":"5699c0f920aa5d9af50bf444cd356d760a14923204ff9ced5128a3e6184ee595"},"schema_version":"1.0","source":{"id":"1809.04683","kind":"arxiv","version":2}},"canonical_sha256":"49b80de203615f53625426cfd8c8444368edcb7a1419536b7bfa6a25ca54505b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"49b80de203615f53625426cfd8c8444368edcb7a1419536b7bfa6a25ca54505b","first_computed_at":"2026-05-18T00:00:43.048383Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:43.048383Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WR6FJzMrN4KeQUSdm4fpk5mH2B0RGfbfA85i/1zfj2y+SCroJxnvKlm1hgBL4ybjHlWmzzsCkBuXYb3Nga9rAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:43.048774Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.04683","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dce9ee90a33f644b2bc454a6f65a4fb1e7196729d5e38197c4d7729c9f0fa5bf","sha256:1dd7563105cf3427a6bcfad871b1660713415508a6b2eb9a5ab3972a1ed87394"],"state_sha256":"6da9b13e54f8d98a956f60b566ff80d34b19cbeb4f3f4683297456d2d4f18d87"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YKKBDdHYr7jHgCOD3sQEC7UiQ9sZXRNqKmXDd+tKCx4/ryzmXYFdRuvTUd5pDDrd7zpHtEDOOIMtiFRKVOtFAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T21:49:17.858435Z","bundle_sha256":"00f8f8a311c7222cdce41b9078ab45abf8c674304d76e2ebd2031b1e45ae2a5b"}}