{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:DZNUIE74WQWL45MACQNGSXR3UA","short_pith_number":"pith:DZNUIE74","canonical_record":{"source":{"id":"2605.30786","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-29T03:21:14Z","cross_cats_sorted":[],"title_canon_sha256":"d47e4ec4e36a37243ddbe96c62b82a065324e5265db4190975f55f6ff2468e7d","abstract_canon_sha256":"f2eddebf6923b684fbc477d9f16a954d18dc0d5f76d6b946928ea0943d0c09f0"},"schema_version":"1.0"},"canonical_sha256":"1e5b4413fcb42cbe7580141a695e3ba012fb43cfad88f1f9eea66d33c68a790b","source":{"kind":"arxiv","id":"2605.30786","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.30786","created_at":"2026-06-01T01:03:16Z"},{"alias_kind":"arxiv_version","alias_value":"2605.30786v1","created_at":"2026-06-01T01:03:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30786","created_at":"2026-06-01T01:03:16Z"},{"alias_kind":"pith_short_12","alias_value":"DZNUIE74WQWL","created_at":"2026-06-01T01:03:16Z"},{"alias_kind":"pith_short_16","alias_value":"DZNUIE74WQWL45MA","created_at":"2026-06-01T01:03:16Z"},{"alias_kind":"pith_short_8","alias_value":"DZNUIE74","created_at":"2026-06-01T01:03:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:DZNUIE74WQWL45MACQNGSXR3UA","target":"record","payload":{"canonical_record":{"source":{"id":"2605.30786","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-29T03:21:14Z","cross_cats_sorted":[],"title_canon_sha256":"d47e4ec4e36a37243ddbe96c62b82a065324e5265db4190975f55f6ff2468e7d","abstract_canon_sha256":"f2eddebf6923b684fbc477d9f16a954d18dc0d5f76d6b946928ea0943d0c09f0"},"schema_version":"1.0"},"canonical_sha256":"1e5b4413fcb42cbe7580141a695e3ba012fb43cfad88f1f9eea66d33c68a790b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:03:16.538512Z","signature_b64":"zv7niaDch+DP/mVf0+wzHacdAKdRHAhGhj1z25dhrhQ/SD3poP7lyLKSsEnI+J4EpEk0FjhrucczVVDPF/kYCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e5b4413fcb42cbe7580141a695e3ba012fb43cfad88f1f9eea66d33c68a790b","last_reissued_at":"2026-06-01T01:03:16.537872Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:03:16.537872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.30786","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":"2026-06-01T01:03:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wwVucNQrxL3X9BEYUlOGJuQGndEx/VwKsDKwToxy0wY1ZEWQyMeAmthoPtppznticaxYds1OfkMMLJyUpGFmCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T12:47:51.338772Z"},"content_sha256":"84c3e7bd86b4de9d6b87575a2174ae804ec684bae423be42e9ed88431f6c9e53","schema_version":"1.0","event_id":"sha256:84c3e7bd86b4de9d6b87575a2174ae804ec684bae423be42e9ed88431f6c9e53"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:DZNUIE74WQWL45MACQNGSXR3UA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chuan Zhou, Ge Zhang, Lixin Zou, Peng Zhang, Shichao Zhu, Shirui Pan, Shuai Zhang, Xixun Lin, Yanan Cao, Yancheng Chen, Zhengyin Zhang, Zhiheng Zhou","submitted_at":"2026-05-29T03:21:14Z","abstract_excerpt":"Graph classification is a core task in graph data mining with widespread real-world applications. Recent advances in graph neural networks (GNNs) have led to substantial performance improvements for graph classification. However, existing GNNs are typically forced to make predictions even under high uncertainty or unknown conditions, resulting in unreliable decisions that can severely impact downstream tasks, particularly in safety-critical scenarios. To address this critical limitation, we propose AbstainGNN, a novel and theory-driven framework for graph classification with abstention, which "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30786","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/2605.30786/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":"2026-06-01T01:03:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c3OSitHTySk5N+Rn+SgEbEzAPXHgbAKGgr0nlmm4MKJT3D7QRBYCeXqURqUHxX8C62BkfoQH0GiJAwgOVfr3Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T12:47:51.339144Z"},"content_sha256":"083940ed764e3dc222e0e4771e0f0b04161f02746b0dc0d07ed15f4a7956d281","schema_version":"1.0","event_id":"sha256:083940ed764e3dc222e0e4771e0f0b04161f02746b0dc0d07ed15f4a7956d281"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DZNUIE74WQWL45MACQNGSXR3UA/bundle.json","state_url":"https://pith.science/pith/DZNUIE74WQWL45MACQNGSXR3UA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DZNUIE74WQWL45MACQNGSXR3UA/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":"2026-06-03T12:47:51Z","links":{"resolver":"https://pith.science/pith/DZNUIE74WQWL45MACQNGSXR3UA","bundle":"https://pith.science/pith/DZNUIE74WQWL45MACQNGSXR3UA/bundle.json","state":"https://pith.science/pith/DZNUIE74WQWL45MACQNGSXR3UA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DZNUIE74WQWL45MACQNGSXR3UA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:DZNUIE74WQWL45MACQNGSXR3UA","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":"f2eddebf6923b684fbc477d9f16a954d18dc0d5f76d6b946928ea0943d0c09f0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-29T03:21:14Z","title_canon_sha256":"d47e4ec4e36a37243ddbe96c62b82a065324e5265db4190975f55f6ff2468e7d"},"schema_version":"1.0","source":{"id":"2605.30786","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.30786","created_at":"2026-06-01T01:03:16Z"},{"alias_kind":"arxiv_version","alias_value":"2605.30786v1","created_at":"2026-06-01T01:03:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30786","created_at":"2026-06-01T01:03:16Z"},{"alias_kind":"pith_short_12","alias_value":"DZNUIE74WQWL","created_at":"2026-06-01T01:03:16Z"},{"alias_kind":"pith_short_16","alias_value":"DZNUIE74WQWL45MA","created_at":"2026-06-01T01:03:16Z"},{"alias_kind":"pith_short_8","alias_value":"DZNUIE74","created_at":"2026-06-01T01:03:16Z"}],"graph_snapshots":[{"event_id":"sha256:083940ed764e3dc222e0e4771e0f0b04161f02746b0dc0d07ed15f4a7956d281","target":"graph","created_at":"2026-06-01T01:03:16Z","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/2605.30786/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph classification is a core task in graph data mining with widespread real-world applications. Recent advances in graph neural networks (GNNs) have led to substantial performance improvements for graph classification. However, existing GNNs are typically forced to make predictions even under high uncertainty or unknown conditions, resulting in unreliable decisions that can severely impact downstream tasks, particularly in safety-critical scenarios. To address this critical limitation, we propose AbstainGNN, a novel and theory-driven framework for graph classification with abstention, which ","authors_text":"Chuan Zhou, Ge Zhang, Lixin Zou, Peng Zhang, Shichao Zhu, Shirui Pan, Shuai Zhang, Xixun Lin, Yanan Cao, Yancheng Chen, Zhengyin Zhang, Zhiheng Zhou","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-29T03:21:14Z","title":"AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30786","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":"sha256:84c3e7bd86b4de9d6b87575a2174ae804ec684bae423be42e9ed88431f6c9e53","target":"record","created_at":"2026-06-01T01:03:16Z","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":"f2eddebf6923b684fbc477d9f16a954d18dc0d5f76d6b946928ea0943d0c09f0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-29T03:21:14Z","title_canon_sha256":"d47e4ec4e36a37243ddbe96c62b82a065324e5265db4190975f55f6ff2468e7d"},"schema_version":"1.0","source":{"id":"2605.30786","kind":"arxiv","version":1}},"canonical_sha256":"1e5b4413fcb42cbe7580141a695e3ba012fb43cfad88f1f9eea66d33c68a790b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1e5b4413fcb42cbe7580141a695e3ba012fb43cfad88f1f9eea66d33c68a790b","first_computed_at":"2026-06-01T01:03:16.537872Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-01T01:03:16.537872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zv7niaDch+DP/mVf0+wzHacdAKdRHAhGhj1z25dhrhQ/SD3poP7lyLKSsEnI+J4EpEk0FjhrucczVVDPF/kYCQ==","signature_status":"signed_v1","signed_at":"2026-06-01T01:03:16.538512Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.30786","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:84c3e7bd86b4de9d6b87575a2174ae804ec684bae423be42e9ed88431f6c9e53","sha256:083940ed764e3dc222e0e4771e0f0b04161f02746b0dc0d07ed15f4a7956d281"],"state_sha256":"841f2af9649c0e1118f1913c20d813440762af9d63e21c1e479f27a9f54281f0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e9rZHugAgGanzjpxL3w5bq1KRjgcxHS9gWxBt+MAthxMgi5Yz8DZvRJiEjJzvIENCXEPxVuRgcwy7HRqO6AeDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T12:47:51.341060Z","bundle_sha256":"e369905e44a4d3fdfa4db48b4986668ad9d695e94aa0744fcb04d5a9dbcea488"}}