{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:Y75H2WCMUPJU3Z22EU4JKPFMTC","short_pith_number":"pith:Y75H2WCM","canonical_record":{"source":{"id":"2203.15470","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-03-29T12:16:38Z","cross_cats_sorted":["cs.LG","q-fin.ST","stat.AP","stat.ME"],"title_canon_sha256":"0f37ba8d9faf7900194fe0cd621fa3a63081da5a9ea19ebb0c9be42441f9b693","abstract_canon_sha256":"a1a7d3737b698a95a5d8c05f084fd2ecf1a684d9fc898425f5957c586d47ed4c"},"schema_version":"1.0"},"canonical_sha256":"c7fa7d584ca3d34de75a2538953cac98b04b59e5f06e03977fd32f6bba0b836d","source":{"kind":"arxiv","id":"2203.15470","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.15470","created_at":"2026-07-05T04:09:25Z"},{"alias_kind":"arxiv_version","alias_value":"2203.15470v1","created_at":"2026-07-05T04:09:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.15470","created_at":"2026-07-05T04:09:25Z"},{"alias_kind":"pith_short_12","alias_value":"Y75H2WCMUPJU","created_at":"2026-07-05T04:09:25Z"},{"alias_kind":"pith_short_16","alias_value":"Y75H2WCMUPJU3Z22","created_at":"2026-07-05T04:09:25Z"},{"alias_kind":"pith_short_8","alias_value":"Y75H2WCM","created_at":"2026-07-05T04:09:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:Y75H2WCMUPJU3Z22EU4JKPFMTC","target":"record","payload":{"canonical_record":{"source":{"id":"2203.15470","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-03-29T12:16:38Z","cross_cats_sorted":["cs.LG","q-fin.ST","stat.AP","stat.ME"],"title_canon_sha256":"0f37ba8d9faf7900194fe0cd621fa3a63081da5a9ea19ebb0c9be42441f9b693","abstract_canon_sha256":"a1a7d3737b698a95a5d8c05f084fd2ecf1a684d9fc898425f5957c586d47ed4c"},"schema_version":"1.0"},"canonical_sha256":"c7fa7d584ca3d34de75a2538953cac98b04b59e5f06e03977fd32f6bba0b836d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:09:25.874518Z","signature_b64":"TXtVKQ0ZpEy0H10gIo4DoZ3fwcyFazimjVXdsenj6xGZAxN3H+ng2Lv2KVRFzSYWpQo6eGdNZA1ClxNTXXa4DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7fa7d584ca3d34de75a2538953cac98b04b59e5f06e03977fd32f6bba0b836d","last_reissued_at":"2026-07-05T04:09:25.874072Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:09:25.874072Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.15470","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:09:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+b5/3aiSEcafCYIVLwEtAt3EOViH0D/gqMz4lOr2e5keZ3WFNp3zOzbG29u0PpPaTP9NGtI1ZpUz+W5FNRuyAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T05:36:38.365949Z"},"content_sha256":"a5fde746fee9223e0cde8a89e6ed7403ef6fc15938ee9fc73e9280337534289a","schema_version":"1.0","event_id":"sha256:a5fde746fee9223e0cde8a89e6ed7403ef6fc15938ee9fc73e9280337534289a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:Y75H2WCMUPJU3Z22EU4JKPFMTC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph similarity learning for change-point detection in dynamic networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-fin.ST","stat.AP","stat.ME"],"primary_cat":"stat.ML","authors_text":"Deborah Sulem, Henry Kenlay, Mihai Cucuringu, Xiaowen Dong","submitted_at":"2022-03-29T12:16:38Z","abstract_excerpt":"Dynamic networks are ubiquitous for modelling sequential graph-structured data, e.g., brain connectome, population flows and messages exchanges. In this work, we consider dynamic networks that are temporal sequences of graph snapshots, and aim at detecting abrupt changes in their structure. This task is often termed network change-point detection and has numerous applications, such as fraud detection or physical motion monitoring. Leveraging a graph neural network model, we design a method to perform online network change-point detection that can adapt to the specific network domain and locali"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.15470","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/2203.15470/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":"2026-07-05T04:09:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MJhF8AeogskH2GBaSnEkZkmnZw1X+sXHnfmgqRc932aL/yga1Dl2oX2t6F23tInoBOHOaTA1vYIQW0EXlvFEDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T05:36:38.366482Z"},"content_sha256":"2c96676afda8ee909134bda13820cd7496fc4ddf62170de0cc280a33e59468df","schema_version":"1.0","event_id":"sha256:2c96676afda8ee909134bda13820cd7496fc4ddf62170de0cc280a33e59468df"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y75H2WCMUPJU3Z22EU4JKPFMTC/bundle.json","state_url":"https://pith.science/pith/Y75H2WCMUPJU3Z22EU4JKPFMTC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y75H2WCMUPJU3Z22EU4JKPFMTC/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":"2026-08-21T05:36:38Z","links":{"resolver":"https://pith.science/pith/Y75H2WCMUPJU3Z22EU4JKPFMTC","bundle":"https://pith.science/pith/Y75H2WCMUPJU3Z22EU4JKPFMTC/bundle.json","state":"https://pith.science/pith/Y75H2WCMUPJU3Z22EU4JKPFMTC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y75H2WCMUPJU3Z22EU4JKPFMTC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:Y75H2WCMUPJU3Z22EU4JKPFMTC","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":"a1a7d3737b698a95a5d8c05f084fd2ecf1a684d9fc898425f5957c586d47ed4c","cross_cats_sorted":["cs.LG","q-fin.ST","stat.AP","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-03-29T12:16:38Z","title_canon_sha256":"0f37ba8d9faf7900194fe0cd621fa3a63081da5a9ea19ebb0c9be42441f9b693"},"schema_version":"1.0","source":{"id":"2203.15470","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.15470","created_at":"2026-07-05T04:09:25Z"},{"alias_kind":"arxiv_version","alias_value":"2203.15470v1","created_at":"2026-07-05T04:09:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.15470","created_at":"2026-07-05T04:09:25Z"},{"alias_kind":"pith_short_12","alias_value":"Y75H2WCMUPJU","created_at":"2026-07-05T04:09:25Z"},{"alias_kind":"pith_short_16","alias_value":"Y75H2WCMUPJU3Z22","created_at":"2026-07-05T04:09:25Z"},{"alias_kind":"pith_short_8","alias_value":"Y75H2WCM","created_at":"2026-07-05T04:09:25Z"}],"graph_snapshots":[{"event_id":"sha256:2c96676afda8ee909134bda13820cd7496fc4ddf62170de0cc280a33e59468df","target":"graph","created_at":"2026-07-05T04:09:25Z","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/2203.15470/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dynamic networks are ubiquitous for modelling sequential graph-structured data, e.g., brain connectome, population flows and messages exchanges. In this work, we consider dynamic networks that are temporal sequences of graph snapshots, and aim at detecting abrupt changes in their structure. This task is often termed network change-point detection and has numerous applications, such as fraud detection or physical motion monitoring. Leveraging a graph neural network model, we design a method to perform online network change-point detection that can adapt to the specific network domain and locali","authors_text":"Deborah Sulem, Henry Kenlay, Mihai Cucuringu, Xiaowen Dong","cross_cats":["cs.LG","q-fin.ST","stat.AP","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-03-29T12:16:38Z","title":"Graph similarity learning for change-point detection in dynamic networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.15470","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:a5fde746fee9223e0cde8a89e6ed7403ef6fc15938ee9fc73e9280337534289a","target":"record","created_at":"2026-07-05T04:09:25Z","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":"a1a7d3737b698a95a5d8c05f084fd2ecf1a684d9fc898425f5957c586d47ed4c","cross_cats_sorted":["cs.LG","q-fin.ST","stat.AP","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-03-29T12:16:38Z","title_canon_sha256":"0f37ba8d9faf7900194fe0cd621fa3a63081da5a9ea19ebb0c9be42441f9b693"},"schema_version":"1.0","source":{"id":"2203.15470","kind":"arxiv","version":1}},"canonical_sha256":"c7fa7d584ca3d34de75a2538953cac98b04b59e5f06e03977fd32f6bba0b836d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c7fa7d584ca3d34de75a2538953cac98b04b59e5f06e03977fd32f6bba0b836d","first_computed_at":"2026-07-05T04:09:25.874072Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:09:25.874072Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TXtVKQ0ZpEy0H10gIo4DoZ3fwcyFazimjVXdsenj6xGZAxN3H+ng2Lv2KVRFzSYWpQo6eGdNZA1ClxNTXXa4DA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:09:25.874518Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.15470","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a5fde746fee9223e0cde8a89e6ed7403ef6fc15938ee9fc73e9280337534289a","sha256:2c96676afda8ee909134bda13820cd7496fc4ddf62170de0cc280a33e59468df"],"state_sha256":"d535a37a5fb43fcd6d58191dc6cf4ba9c03ec6b7eef383da1e546d3f26e7e0de"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HJA6cIDejGiTXmO5XUdr6CHqmN2h7GFr9h11oib/Y55oAlkKngjujlc+FLK4iTnd4Efm0dQfQ2Ytw+5UEzFYBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T05:36:38.370583Z","bundle_sha256":"83ee6313d37e43a695c61aa1cafd2ee21dbc32ef0dc46416e0ec579bf6fd0dc0"}}