{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:4ALRRTVUDTOX5LTEB4IN6S34BX","short_pith_number":"pith:4ALRRTVU","canonical_record":{"source":{"id":"2308.02353","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-08-04T14:41:03Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a464525054ef438d80cb7776662144a2ad115c6f9c75edd8012559b0ff86c665","abstract_canon_sha256":"225bd8e13f8e4fce59d92104ad2ed814fd21f80935b8de144fff5bbcb72e472d"},"schema_version":"1.0"},"canonical_sha256":"e01718ceb41cdd7eae640f10df4b7c0dcf9d55a9dc7265f58ea2c066482fefbc","source":{"kind":"arxiv","id":"2308.02353","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.02353","created_at":"2026-07-05T06:37:41Z"},{"alias_kind":"arxiv_version","alias_value":"2308.02353v1","created_at":"2026-07-05T06:37:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.02353","created_at":"2026-07-05T06:37:41Z"},{"alias_kind":"pith_short_12","alias_value":"4ALRRTVUDTOX","created_at":"2026-07-05T06:37:41Z"},{"alias_kind":"pith_short_16","alias_value":"4ALRRTVUDTOX5LTE","created_at":"2026-07-05T06:37:41Z"},{"alias_kind":"pith_short_8","alias_value":"4ALRRTVU","created_at":"2026-07-05T06:37:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:4ALRRTVUDTOX5LTEB4IN6S34BX","target":"record","payload":{"canonical_record":{"source":{"id":"2308.02353","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-08-04T14:41:03Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a464525054ef438d80cb7776662144a2ad115c6f9c75edd8012559b0ff86c665","abstract_canon_sha256":"225bd8e13f8e4fce59d92104ad2ed814fd21f80935b8de144fff5bbcb72e472d"},"schema_version":"1.0"},"canonical_sha256":"e01718ceb41cdd7eae640f10df4b7c0dcf9d55a9dc7265f58ea2c066482fefbc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:37:41.530915Z","signature_b64":"3SVaGwqmLtWGiGPs2uMDZRhrlpiKP76MigoKFtOhjfgHRmxJSDfUjAtDv3Ql/m3HLUkqdSr9v2znq1qO4uC8Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e01718ceb41cdd7eae640f10df4b7c0dcf9d55a9dc7265f58ea2c066482fefbc","last_reissued_at":"2026-07-05T06:37:41.530392Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:37:41.530392Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2308.02353","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":"2026-07-05T06:37:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qu08N24/Qtuh5JVj6x//+hIfz+OVhBjSBLtElgdk17ExqvZ0nhLn51t2avBiYq4ubTGbwCC75g/+O3uO9JJ/Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T12:46:11.516513Z"},"content_sha256":"2e11d0a113915c5c4e6ea2698aeed6636ee864128c1bd3327f0bc6409aada495","schema_version":"1.0","event_id":"sha256:2e11d0a113915c5c4e6ea2698aeed6636ee864128c1bd3327f0bc6409aada495"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:4ALRRTVUDTOX5LTEB4IN6S34BX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bardh Prenkaj, Gjergji Kasneci, Mario Villaizan-Vallelado, Tobias Leemann","submitted_at":"2023-08-04T14:41:03Z","abstract_excerpt":"We introduce a novel semi-supervised Graph Counterfactual Explainer (GCE) methodology, Dynamic GRAph Counterfactual Explainer (DyGRACE). It leverages initial knowledge about the data distribution to search for valid counterfactuals while avoiding using information from potentially outdated decision functions in subsequent time steps. Employing two graph autoencoders (GAEs), DyGRACE learns the representation of each class in a binary classification scenario. The GAEs minimise the reconstruction error between the original graph and its learned representation during training. The method involves "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.02353","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/2308.02353/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":"2026-07-05T06:37:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ditl4/94/Tcz1ItAl92WyRVI2esC8MEAwKjxVXTccmICSbo+EYq7u2mEsmHUq+GF2NVtDGDJ8Q42Rc/EFt7RAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T12:46:11.517028Z"},"content_sha256":"526f71cbf64e3dddb5f07de08dd082b7dbe35cea0b0dbcb3647535f9abeaaed0","schema_version":"1.0","event_id":"sha256:526f71cbf64e3dddb5f07de08dd082b7dbe35cea0b0dbcb3647535f9abeaaed0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4ALRRTVUDTOX5LTEB4IN6S34BX/bundle.json","state_url":"https://pith.science/pith/4ALRRTVUDTOX5LTEB4IN6S34BX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4ALRRTVUDTOX5LTEB4IN6S34BX/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":"2026-08-06T12:46:11Z","links":{"resolver":"https://pith.science/pith/4ALRRTVUDTOX5LTEB4IN6S34BX","bundle":"https://pith.science/pith/4ALRRTVUDTOX5LTEB4IN6S34BX/bundle.json","state":"https://pith.science/pith/4ALRRTVUDTOX5LTEB4IN6S34BX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4ALRRTVUDTOX5LTEB4IN6S34BX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:4ALRRTVUDTOX5LTEB4IN6S34BX","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":"225bd8e13f8e4fce59d92104ad2ed814fd21f80935b8de144fff5bbcb72e472d","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-08-04T14:41:03Z","title_canon_sha256":"a464525054ef438d80cb7776662144a2ad115c6f9c75edd8012559b0ff86c665"},"schema_version":"1.0","source":{"id":"2308.02353","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.02353","created_at":"2026-07-05T06:37:41Z"},{"alias_kind":"arxiv_version","alias_value":"2308.02353v1","created_at":"2026-07-05T06:37:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.02353","created_at":"2026-07-05T06:37:41Z"},{"alias_kind":"pith_short_12","alias_value":"4ALRRTVUDTOX","created_at":"2026-07-05T06:37:41Z"},{"alias_kind":"pith_short_16","alias_value":"4ALRRTVUDTOX5LTE","created_at":"2026-07-05T06:37:41Z"},{"alias_kind":"pith_short_8","alias_value":"4ALRRTVU","created_at":"2026-07-05T06:37:41Z"}],"graph_snapshots":[{"event_id":"sha256:526f71cbf64e3dddb5f07de08dd082b7dbe35cea0b0dbcb3647535f9abeaaed0","target":"graph","created_at":"2026-07-05T06:37:41Z","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/2308.02353/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce a novel semi-supervised Graph Counterfactual Explainer (GCE) methodology, Dynamic GRAph Counterfactual Explainer (DyGRACE). It leverages initial knowledge about the data distribution to search for valid counterfactuals while avoiding using information from potentially outdated decision functions in subsequent time steps. Employing two graph autoencoders (GAEs), DyGRACE learns the representation of each class in a binary classification scenario. The GAEs minimise the reconstruction error between the original graph and its learned representation during training. The method involves ","authors_text":"Bardh Prenkaj, Gjergji Kasneci, Mario Villaizan-Vallelado, Tobias Leemann","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-08-04T14:41:03Z","title":"Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.02353","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":"sha256:2e11d0a113915c5c4e6ea2698aeed6636ee864128c1bd3327f0bc6409aada495","target":"record","created_at":"2026-07-05T06:37:41Z","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":"225bd8e13f8e4fce59d92104ad2ed814fd21f80935b8de144fff5bbcb72e472d","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-08-04T14:41:03Z","title_canon_sha256":"a464525054ef438d80cb7776662144a2ad115c6f9c75edd8012559b0ff86c665"},"schema_version":"1.0","source":{"id":"2308.02353","kind":"arxiv","version":1}},"canonical_sha256":"e01718ceb41cdd7eae640f10df4b7c0dcf9d55a9dc7265f58ea2c066482fefbc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e01718ceb41cdd7eae640f10df4b7c0dcf9d55a9dc7265f58ea2c066482fefbc","first_computed_at":"2026-07-05T06:37:41.530392Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:37:41.530392Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3SVaGwqmLtWGiGPs2uMDZRhrlpiKP76MigoKFtOhjfgHRmxJSDfUjAtDv3Ql/m3HLUkqdSr9v2znq1qO4uC8Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:37:41.530915Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.02353","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2e11d0a113915c5c4e6ea2698aeed6636ee864128c1bd3327f0bc6409aada495","sha256:526f71cbf64e3dddb5f07de08dd082b7dbe35cea0b0dbcb3647535f9abeaaed0"],"state_sha256":"0507e81f7634efeb204351f96942f61bc38234d0fda7a2d68555532d48535cf0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eAUx98EC3/4teWJBn/OckXoSMq9DfNpoUwfpGbV6e+kpiMIl+GEfrlsJ1/mRoXbEa2lpPyIhnPmQCwEoP+c9DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T12:46:11.522303Z","bundle_sha256":"b8e41047702c8f7b512a6f0d3ee66baff2d0336e700a61d09b6c540cd0a2e856"}}