{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:H4AGAXGAY5GDNH7OWIMLKPFKPF","short_pith_number":"pith:H4AGAXGA","schema_version":"1.0","canonical_sha256":"3f00605cc0c74c369feeb218b53caa795e6fd0bfc3beeb539de9f05bb7c86ae2","source":{"kind":"arxiv","id":"2106.04714","version":1},"attestation_state":"computed","paper":{"title":"NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Charu Aggarwal, Enyan Dai, Suhang Wang","submitted_at":"2021-06-08T22:12:44Z","abstract_excerpt":"Graph Neural Networks (GNNs) have achieved promising results for semi-supervised learning tasks on graphs such as node classification. Despite the great success of GNNs, many real-world graphs are often sparsely and noisily labeled, which could significantly degrade the performance of GNNs, as the noisy information could propagate to unlabeled nodes via graph structure. Thus, it is important to develop a label noise-resistant GNN for semi-supervised node classification. Though extensive studies have been conducted to learn neural networks with noisy labels, they mostly focus on independent and"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2106.04714","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-08T22:12:44Z","cross_cats_sorted":[],"title_canon_sha256":"0a7c0d8bdd1b191766430e74fbd21089f796e29ad925c64fd7016062562f55e6","abstract_canon_sha256":"73e5890b2fb5e261dc5acda418e00a46687ba991c6c1f4d0dda5720fba387379"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:47:48.315777Z","signature_b64":"NCHOAR8lsaa45hS8cKGv752uC7uk/D8aj5Cna/TnxWkmBoVMu/dSDGYwZmOo70/mNY5dGKGSvmMzk1YccjbiAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f00605cc0c74c369feeb218b53caa795e6fd0bfc3beeb539de9f05bb7c86ae2","last_reissued_at":"2026-07-05T02:47:48.315263Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:47:48.315263Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Charu Aggarwal, Enyan Dai, Suhang Wang","submitted_at":"2021-06-08T22:12:44Z","abstract_excerpt":"Graph Neural Networks (GNNs) have achieved promising results for semi-supervised learning tasks on graphs such as node classification. Despite the great success of GNNs, many real-world graphs are often sparsely and noisily labeled, which could significantly degrade the performance of GNNs, as the noisy information could propagate to unlabeled nodes via graph structure. Thus, it is important to develop a label noise-resistant GNN for semi-supervised node classification. Though extensive studies have been conducted to learn neural networks with noisy labels, they mostly focus on independent and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.04714","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/2106.04714/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2106.04714","created_at":"2026-07-05T02:47:48.315333+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.04714v1","created_at":"2026-07-05T02:47:48.315333+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.04714","created_at":"2026-07-05T02:47:48.315333+00:00"},{"alias_kind":"pith_short_12","alias_value":"H4AGAXGAY5GD","created_at":"2026-07-05T02:47:48.315333+00:00"},{"alias_kind":"pith_short_16","alias_value":"H4AGAXGAY5GDNH7O","created_at":"2026-07-05T02:47:48.315333+00:00"},{"alias_kind":"pith_short_8","alias_value":"H4AGAXGA","created_at":"2026-07-05T02:47:48.315333+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.02451","citing_title":"Weak Supervision for Real World Graphs","ref_index":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H4AGAXGAY5GDNH7OWIMLKPFKPF","json":"https://pith.science/pith/H4AGAXGAY5GDNH7OWIMLKPFKPF.json","graph_json":"https://pith.science/api/pith-number/H4AGAXGAY5GDNH7OWIMLKPFKPF/graph.json","events_json":"https://pith.science/api/pith-number/H4AGAXGAY5GDNH7OWIMLKPFKPF/events.json","paper":"https://pith.science/paper/H4AGAXGA"},"agent_actions":{"view_html":"https://pith.science/pith/H4AGAXGAY5GDNH7OWIMLKPFKPF","download_json":"https://pith.science/pith/H4AGAXGAY5GDNH7OWIMLKPFKPF.json","view_paper":"https://pith.science/paper/H4AGAXGA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.04714&json=true","fetch_graph":"https://pith.science/api/pith-number/H4AGAXGAY5GDNH7OWIMLKPFKPF/graph.json","fetch_events":"https://pith.science/api/pith-number/H4AGAXGAY5GDNH7OWIMLKPFKPF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H4AGAXGAY5GDNH7OWIMLKPFKPF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H4AGAXGAY5GDNH7OWIMLKPFKPF/action/storage_attestation","attest_author":"https://pith.science/pith/H4AGAXGAY5GDNH7OWIMLKPFKPF/action/author_attestation","sign_citation":"https://pith.science/pith/H4AGAXGAY5GDNH7OWIMLKPFKPF/action/citation_signature","submit_replication":"https://pith.science/pith/H4AGAXGAY5GDNH7OWIMLKPFKPF/action/replication_record"}},"created_at":"2026-07-05T02:47:48.315333+00:00","updated_at":"2026-07-05T02:47:48.315333+00:00"}