{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:BSPGHUIOHQWPKZDJBGNY6QSN4Z","short_pith_number":"pith:BSPGHUIO","canonical_record":{"source":{"id":"2601.17469","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-01-24T14:07:07Z","cross_cats_sorted":[],"title_canon_sha256":"4853f44752f9719ddaf232a209d126e48d987804cec0e0ffd4e7412cc08a1bdc","abstract_canon_sha256":"ace90421591cb1d3607ee661606ebe9f7025ac8966a894248989b1cc9192bbcf"},"schema_version":"1.0"},"canonical_sha256":"0c9e63d10e3c2cf56469099b8f424de679edfd7e5940eeb6aeb7a7cdab04eb4c","source":{"kind":"arxiv","id":"2601.17469","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.17469","created_at":"2026-06-04T01:08:40Z"},{"alias_kind":"arxiv_version","alias_value":"2601.17469v2","created_at":"2026-06-04T01:08:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.17469","created_at":"2026-06-04T01:08:40Z"},{"alias_kind":"pith_short_12","alias_value":"BSPGHUIOHQWP","created_at":"2026-06-04T01:08:40Z"},{"alias_kind":"pith_short_16","alias_value":"BSPGHUIOHQWPKZDJ","created_at":"2026-06-04T01:08:40Z"},{"alias_kind":"pith_short_8","alias_value":"BSPGHUIO","created_at":"2026-06-04T01:08:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:BSPGHUIOHQWPKZDJBGNY6QSN4Z","target":"record","payload":{"canonical_record":{"source":{"id":"2601.17469","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-01-24T14:07:07Z","cross_cats_sorted":[],"title_canon_sha256":"4853f44752f9719ddaf232a209d126e48d987804cec0e0ffd4e7412cc08a1bdc","abstract_canon_sha256":"ace90421591cb1d3607ee661606ebe9f7025ac8966a894248989b1cc9192bbcf"},"schema_version":"1.0"},"canonical_sha256":"0c9e63d10e3c2cf56469099b8f424de679edfd7e5940eeb6aeb7a7cdab04eb4c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T01:08:40.398834Z","signature_b64":"dOpBAMRa+SIzib+JUtmPpblUAf8utspAWMLtnRQxwQy/Tos+VLP05NLRfnNqv61vHFAaUgfcWSGY3C9kQQ0TAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c9e63d10e3c2cf56469099b8f424de679edfd7e5940eeb6aeb7a7cdab04eb4c","last_reissued_at":"2026-06-04T01:08:40.397922Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T01:08:40.397922Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2601.17469","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":"2026-06-04T01:08:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2YO3E6JiILixdnbix2odjH5a7kFarptxv8ROT025CA7rFIPIgcJ2YCRlnyMJ2KNWasrQSWQcsuA6KLM1eGg5Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T12:35:24.310772Z"},"content_sha256":"ef346de31da13f16e35e6d29c0fce2aee50bad7022d4fa1bb722cd89a4881265","schema_version":"1.0","event_id":"sha256:ef346de31da13f16e35e6d29c0fce2aee50bad7022d4fa1bb722cd89a4881265"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:BSPGHUIOHQWPKZDJBGNY6QSN4Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jingyang Yuan, Ming Zhang, Siyu Yi, Wei Ju, Wei Zhang, Yifan Wang, Zhengyang Mao, Zhiping Xiao, Ziyue Qiao","submitted_at":"2026-01-24T14:07:07Z","abstract_excerpt":"Graph Neural Networks (GNNs) have shown remarkable capabilities in learning from graph-structured data with various applications such as social analysis and bioinformatics. However, the presence of label noise in real scenarios poses a significant challenge in learning robust GNNs, and their effectiveness can be severely impacted when dealing with noisy labels on graphs, often stemming from annotation errors or inconsistencies. To address this, in this paper we propose a novel approach called ICGNN that harnesses the structure information of the graph to effectively alleviate the challenges po"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.17469","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/2601.17469/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":"2026-06-04T01:08:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MJbOQQaG75P9wSIfW1p2wBHLoiXH6HbNSQcmxczfQWoqd/SjuzzoDapC6vDGMSkmPuQGQ1ffivnZ7jq5IjbdCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T12:35:24.311135Z"},"content_sha256":"347f12a62ca9fc816991925d187deed7962c6a236ecbb461d99487145605145f","schema_version":"1.0","event_id":"sha256:347f12a62ca9fc816991925d187deed7962c6a236ecbb461d99487145605145f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BSPGHUIOHQWPKZDJBGNY6QSN4Z/bundle.json","state_url":"https://pith.science/pith/BSPGHUIOHQWPKZDJBGNY6QSN4Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BSPGHUIOHQWPKZDJBGNY6QSN4Z/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":"2026-06-09T12:35:24Z","links":{"resolver":"https://pith.science/pith/BSPGHUIOHQWPKZDJBGNY6QSN4Z","bundle":"https://pith.science/pith/BSPGHUIOHQWPKZDJBGNY6QSN4Z/bundle.json","state":"https://pith.science/pith/BSPGHUIOHQWPKZDJBGNY6QSN4Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BSPGHUIOHQWPKZDJBGNY6QSN4Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:BSPGHUIOHQWPKZDJBGNY6QSN4Z","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":"ace90421591cb1d3607ee661606ebe9f7025ac8966a894248989b1cc9192bbcf","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-01-24T14:07:07Z","title_canon_sha256":"4853f44752f9719ddaf232a209d126e48d987804cec0e0ffd4e7412cc08a1bdc"},"schema_version":"1.0","source":{"id":"2601.17469","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.17469","created_at":"2026-06-04T01:08:40Z"},{"alias_kind":"arxiv_version","alias_value":"2601.17469v2","created_at":"2026-06-04T01:08:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.17469","created_at":"2026-06-04T01:08:40Z"},{"alias_kind":"pith_short_12","alias_value":"BSPGHUIOHQWP","created_at":"2026-06-04T01:08:40Z"},{"alias_kind":"pith_short_16","alias_value":"BSPGHUIOHQWPKZDJ","created_at":"2026-06-04T01:08:40Z"},{"alias_kind":"pith_short_8","alias_value":"BSPGHUIO","created_at":"2026-06-04T01:08:40Z"}],"graph_snapshots":[{"event_id":"sha256:347f12a62ca9fc816991925d187deed7962c6a236ecbb461d99487145605145f","target":"graph","created_at":"2026-06-04T01:08:40Z","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/2601.17469/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks (GNNs) have shown remarkable capabilities in learning from graph-structured data with various applications such as social analysis and bioinformatics. However, the presence of label noise in real scenarios poses a significant challenge in learning robust GNNs, and their effectiveness can be severely impacted when dealing with noisy labels on graphs, often stemming from annotation errors or inconsistencies. To address this, in this paper we propose a novel approach called ICGNN that harnesses the structure information of the graph to effectively alleviate the challenges po","authors_text":"Jingyang Yuan, Ming Zhang, Siyu Yi, Wei Ju, Wei Zhang, Yifan Wang, Zhengyang Mao, Zhiping Xiao, Ziyue Qiao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-01-24T14:07:07Z","title":"Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.17469","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:ef346de31da13f16e35e6d29c0fce2aee50bad7022d4fa1bb722cd89a4881265","target":"record","created_at":"2026-06-04T01:08:40Z","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":"ace90421591cb1d3607ee661606ebe9f7025ac8966a894248989b1cc9192bbcf","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-01-24T14:07:07Z","title_canon_sha256":"4853f44752f9719ddaf232a209d126e48d987804cec0e0ffd4e7412cc08a1bdc"},"schema_version":"1.0","source":{"id":"2601.17469","kind":"arxiv","version":2}},"canonical_sha256":"0c9e63d10e3c2cf56469099b8f424de679edfd7e5940eeb6aeb7a7cdab04eb4c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0c9e63d10e3c2cf56469099b8f424de679edfd7e5940eeb6aeb7a7cdab04eb4c","first_computed_at":"2026-06-04T01:08:40.397922Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-04T01:08:40.397922Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dOpBAMRa+SIzib+JUtmPpblUAf8utspAWMLtnRQxwQy/Tos+VLP05NLRfnNqv61vHFAaUgfcWSGY3C9kQQ0TAQ==","signature_status":"signed_v1","signed_at":"2026-06-04T01:08:40.398834Z","signed_message":"canonical_sha256_bytes"},"source_id":"2601.17469","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ef346de31da13f16e35e6d29c0fce2aee50bad7022d4fa1bb722cd89a4881265","sha256:347f12a62ca9fc816991925d187deed7962c6a236ecbb461d99487145605145f"],"state_sha256":"2dba973694f824456106a317b12513ac5eb59c5175692f01e45aa27f0d582c91"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ii/+UBN8GMt8KExSgtvIP1bdnrt2BQVdDqQKupoyUXG+KGq3dpRsjKaLWUW4O78G38ej+P4n/yO8rxXAVAGBBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T12:35:24.313156Z","bundle_sha256":"51c443412cca421c762fb5c6c689ccaad9c8abd0405ebfed270d3144d2c3bda1"}}