{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:XD6FHW5MXD5YNS6HV33O3EIIQZ","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":"94477ffe240cf0da176a905f4219daece546b122dfa408510f42a47dc29d732a","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-07-08T19:50:00Z","title_canon_sha256":"512b75fa7d1b9b050324621473583fcf4e28ebd0a02bfab88d0e82e94b85816a"},"schema_version":"1.0","source":{"id":"2107.04086","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.04086","created_at":"2026-07-05T04:39:46Z"},{"alias_kind":"arxiv_version","alias_value":"2107.04086v3","created_at":"2026-07-05T04:39:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.04086","created_at":"2026-07-05T04:39:46Z"},{"alias_kind":"pith_short_12","alias_value":"XD6FHW5MXD5Y","created_at":"2026-07-05T04:39:46Z"},{"alias_kind":"pith_short_16","alias_value":"XD6FHW5MXD5YNS6H","created_at":"2026-07-05T04:39:46Z"},{"alias_kind":"pith_short_8","alias_value":"XD6FHW5M","created_at":"2026-07-05T04:39:46Z"}],"graph_snapshots":[{"event_id":"sha256:c12a4f0de4a39874c0cbeaf5f07b34f6392fd3c3ec285d432fc377a60de7d375","target":"graph","created_at":"2026-07-05T04:39:46Z","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/2107.04086/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuition. Most existing methods generate explanations by identifying a subgraph of an input graph that has a strong correlation with the prediction. These explanations are not robust to noise because independently optimizing the correlation for a single input can easily overfit noise. Moreover, they do not align well with human intuition because removing an identified subgraph from an input graph does not necessarily change","authors_text":"Jian Pei, Lanjun Wang, Lingyang Chu, Mohit Bajaj, Peter Cho-Ho Lam, Yong Zhang, Zi Yu Xue","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-07-08T19:50:00Z","title":"Robust Counterfactual Explanations on Graph Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.04086","kind":"arxiv","version":3},"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:a9367c7418c9dddf9127cd39d5dadf42e3e3c8ebe45cf1a0d051b74e7f701e3b","target":"record","created_at":"2026-07-05T04:39:46Z","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":"94477ffe240cf0da176a905f4219daece546b122dfa408510f42a47dc29d732a","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-07-08T19:50:00Z","title_canon_sha256":"512b75fa7d1b9b050324621473583fcf4e28ebd0a02bfab88d0e82e94b85816a"},"schema_version":"1.0","source":{"id":"2107.04086","kind":"arxiv","version":3}},"canonical_sha256":"b8fc53dbacb8fb86cbc7aef6ed91088669d4a52b340cd16de3918d02495556d4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b8fc53dbacb8fb86cbc7aef6ed91088669d4a52b340cd16de3918d02495556d4","first_computed_at":"2026-07-05T04:39:46.839563Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:39:46.839563Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CF8NQGDQZ9ctPOWzKpXauyOdanAcNEoC3ijZmLsdRXuH3/G7rR7w6neg/ykJsvfuZmhTCYUAoZp1uB/29VwAAA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:39:46.840017Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.04086","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a9367c7418c9dddf9127cd39d5dadf42e3e3c8ebe45cf1a0d051b74e7f701e3b","sha256:c12a4f0de4a39874c0cbeaf5f07b34f6392fd3c3ec285d432fc377a60de7d375"],"state_sha256":"7b9349536c6bfa332cd64df59b0aca539bf7f960c4403a963d4d6699efb43322"}