{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:X3FDIWV4WBYOHBANYHUSMUWLA5","short_pith_number":"pith:X3FDIWV4","canonical_record":{"source":{"id":"2403.07185","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-11T21:54:52Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"65c656d46a2f000a8b6e0f78a373539b02b1bd4c9bc9c98ae4fcb08165cd594e","abstract_canon_sha256":"6316d2d0f9e7c3b53c154ea5891462086cfa8918011b511fab769ab5b72e4171"},"schema_version":"1.0"},"canonical_sha256":"beca345abcb070e3840dc1e92652cb075f564a0051592d0ce93da90f4d788646","source":{"kind":"arxiv","id":"2403.07185","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.07185","created_at":"2026-07-05T10:27:08Z"},{"alias_kind":"arxiv_version","alias_value":"2403.07185v2","created_at":"2026-07-05T10:27:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.07185","created_at":"2026-07-05T10:27:08Z"},{"alias_kind":"pith_short_12","alias_value":"X3FDIWV4WBYO","created_at":"2026-07-05T10:27:08Z"},{"alias_kind":"pith_short_16","alias_value":"X3FDIWV4WBYOHBAN","created_at":"2026-07-05T10:27:08Z"},{"alias_kind":"pith_short_8","alias_value":"X3FDIWV4","created_at":"2026-07-05T10:27:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:X3FDIWV4WBYOHBANYHUSMUWLA5","target":"record","payload":{"canonical_record":{"source":{"id":"2403.07185","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-11T21:54:52Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"65c656d46a2f000a8b6e0f78a373539b02b1bd4c9bc9c98ae4fcb08165cd594e","abstract_canon_sha256":"6316d2d0f9e7c3b53c154ea5891462086cfa8918011b511fab769ab5b72e4171"},"schema_version":"1.0"},"canonical_sha256":"beca345abcb070e3840dc1e92652cb075f564a0051592d0ce93da90f4d788646","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:27:08.533240Z","signature_b64":"NL58AIb6yt9SQb57+EeHOCpAuUs03DDGlg/aihWqPBxCmN1wy4fEyieKPsucOTgncORoDADdmWM3xcVwxL/DDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"beca345abcb070e3840dc1e92652cb075f564a0051592d0ce93da90f4d788646","last_reissued_at":"2026-07-05T10:27:08.532702Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:27:08.532702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.07185","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":"2026-07-05T10:27:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bjEOzlap9V/frXh771ohxQ5ICHXiQ2MG94jbRR4zHcQCmGOqHqdBJ2lnFJGufuvCFwFCfe66Q5sS8kiE2KtuBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T12:49:54.407156Z"},"content_sha256":"e2c1786b546513f4b9937f4f612e2245198c0ff3b18e777da024909bd444f404","schema_version":"1.0","event_id":"sha256:e2c1786b546513f4b9937f4f612e2245198c0ff3b18e777da024909bd444f404"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:X3FDIWV4WBYOHBANYHUSMUWLA5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Uncertainty in Graph Neural Networks: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Fangxin Wang, Kay Liu, Philip S. Yu, Sourav Medya, Yibo Wang, Yuqing Liu","submitted_at":"2024-03-11T21:54:52Z","abstract_excerpt":"Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources such as inherent randomness in data and model training errors can lead to unstable and erroneous predictions. Therefore, identifying, quantifying, and utilizing uncertainty are essential to enhance the performance of the model for the downstream tasks as well as the reliability of the GNN predictions. This survey aims to provide a comprehensive overview of the GNNs from the perspective of uncertainty with an emphasis on its integr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.07185","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2403.07185/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":"2026-07-05T10:27:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"56zfYpF8+xjEmprO7WhxTqP4WNCFOUAE1uM8rdrUqnTF7rt+IXAEmJBOSayb2CbGL7lJFEGcJHj8hjI7QjpxAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T12:49:54.407738Z"},"content_sha256":"d68881d9f23482888eb9642a218c9e1c8bb06d5134424f73143939089449d32d","schema_version":"1.0","event_id":"sha256:d68881d9f23482888eb9642a218c9e1c8bb06d5134424f73143939089449d32d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X3FDIWV4WBYOHBANYHUSMUWLA5/bundle.json","state_url":"https://pith.science/pith/X3FDIWV4WBYOHBANYHUSMUWLA5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X3FDIWV4WBYOHBANYHUSMUWLA5/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":"2026-08-08T12:49:54Z","links":{"resolver":"https://pith.science/pith/X3FDIWV4WBYOHBANYHUSMUWLA5","bundle":"https://pith.science/pith/X3FDIWV4WBYOHBANYHUSMUWLA5/bundle.json","state":"https://pith.science/pith/X3FDIWV4WBYOHBANYHUSMUWLA5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X3FDIWV4WBYOHBANYHUSMUWLA5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:X3FDIWV4WBYOHBANYHUSMUWLA5","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":"6316d2d0f9e7c3b53c154ea5891462086cfa8918011b511fab769ab5b72e4171","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-11T21:54:52Z","title_canon_sha256":"65c656d46a2f000a8b6e0f78a373539b02b1bd4c9bc9c98ae4fcb08165cd594e"},"schema_version":"1.0","source":{"id":"2403.07185","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.07185","created_at":"2026-07-05T10:27:08Z"},{"alias_kind":"arxiv_version","alias_value":"2403.07185v2","created_at":"2026-07-05T10:27:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.07185","created_at":"2026-07-05T10:27:08Z"},{"alias_kind":"pith_short_12","alias_value":"X3FDIWV4WBYO","created_at":"2026-07-05T10:27:08Z"},{"alias_kind":"pith_short_16","alias_value":"X3FDIWV4WBYOHBAN","created_at":"2026-07-05T10:27:08Z"},{"alias_kind":"pith_short_8","alias_value":"X3FDIWV4","created_at":"2026-07-05T10:27:08Z"}],"graph_snapshots":[{"event_id":"sha256:d68881d9f23482888eb9642a218c9e1c8bb06d5134424f73143939089449d32d","target":"graph","created_at":"2026-07-05T10:27:08Z","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/2403.07185/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources such as inherent randomness in data and model training errors can lead to unstable and erroneous predictions. Therefore, identifying, quantifying, and utilizing uncertainty are essential to enhance the performance of the model for the downstream tasks as well as the reliability of the GNN predictions. This survey aims to provide a comprehensive overview of the GNNs from the perspective of uncertainty with an emphasis on its integr","authors_text":"Fangxin Wang, Kay Liu, Philip S. Yu, Sourav Medya, Yibo Wang, Yuqing Liu","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-11T21:54:52Z","title":"Uncertainty in Graph Neural Networks: A Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.07185","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:e2c1786b546513f4b9937f4f612e2245198c0ff3b18e777da024909bd444f404","target":"record","created_at":"2026-07-05T10:27:08Z","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":"6316d2d0f9e7c3b53c154ea5891462086cfa8918011b511fab769ab5b72e4171","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-11T21:54:52Z","title_canon_sha256":"65c656d46a2f000a8b6e0f78a373539b02b1bd4c9bc9c98ae4fcb08165cd594e"},"schema_version":"1.0","source":{"id":"2403.07185","kind":"arxiv","version":2}},"canonical_sha256":"beca345abcb070e3840dc1e92652cb075f564a0051592d0ce93da90f4d788646","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"beca345abcb070e3840dc1e92652cb075f564a0051592d0ce93da90f4d788646","first_computed_at":"2026-07-05T10:27:08.532702Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:27:08.532702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NL58AIb6yt9SQb57+EeHOCpAuUs03DDGlg/aihWqPBxCmN1wy4fEyieKPsucOTgncORoDADdmWM3xcVwxL/DDg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:27:08.533240Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.07185","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e2c1786b546513f4b9937f4f612e2245198c0ff3b18e777da024909bd444f404","sha256:d68881d9f23482888eb9642a218c9e1c8bb06d5134424f73143939089449d32d"],"state_sha256":"02de75392da97d6a23b25d4b25cfa335fc497cf7bbc52054c558c0febead3567"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rpL87gHajAbWpljMND325ZdW3+7ogpVZj/h8wOyE0d86DMhZVcAjYmLNtu9am99WcmnJzd3EITRmhp8ErMhEAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T12:49:54.413651Z","bundle_sha256":"a35bf3f9cdf262392ba51672112fc78554c562b644c04f95bf9efc322c13dddb"}}