{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:Y65VI26FYYCP4LN67D2DMOB2O2","short_pith_number":"pith:Y65VI26F","schema_version":"1.0","canonical_sha256":"c7bb546bc5c604fe2dbef8f436383a76b5b95b2b11a277f70a820014875cfcdc","source":{"kind":"arxiv","id":"2502.06280","version":1},"attestation_state":"computed","paper":{"title":"IceBerg: Debiased Self-Training for Class-Imbalanced Node Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Daixin Wang, Dingshuo Chen, Hongrui Liu, Jeffrey Xu Yu, Jun Zhou, Tong Zhao, Zhiqiang Zhang, Zhixun Li","submitted_at":"2025-02-10T09:23:22Z","abstract_excerpt":"Graph Neural Networks (GNNs) have achieved great success in dealing with non-Euclidean graph-structured data and have been widely deployed in many real-world applications. However, their effectiveness is often jeopardized under class-imbalanced training sets. Most existing studies have analyzed class-imbalanced node classification from a supervised learning perspective, but they do not fully utilize the large number of unlabeled nodes in semi-supervised scenarios. We claim that the supervised signal is just the tip of the iceberg and a large number of unlabeled nodes have not yet been effectiv"},"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":"2502.06280","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-10T09:23:22Z","cross_cats_sorted":[],"title_canon_sha256":"d8a9c4064271f47bdfb44af888b062e17734a922dcde42e22faa8482b824134e","abstract_canon_sha256":"fd1153666c16fdadc04a4b489a8f1ca5e5628e02af099855702742ff1ed9e76b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:11:49.957762Z","signature_b64":"8wvV9jwzgo9mVvXONMRnOH4gQHMac+VhBibhGlPl3QBz6ESBc+kcDFGtxlu8a1Jx3UGyqjkk66WwuwS1yqgABA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7bb546bc5c604fe2dbef8f436383a76b5b95b2b11a277f70a820014875cfcdc","last_reissued_at":"2026-07-05T10:11:49.957217Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:11:49.957217Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"IceBerg: Debiased Self-Training for Class-Imbalanced Node Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Daixin Wang, Dingshuo Chen, Hongrui Liu, Jeffrey Xu Yu, Jun Zhou, Tong Zhao, Zhiqiang Zhang, Zhixun Li","submitted_at":"2025-02-10T09:23:22Z","abstract_excerpt":"Graph Neural Networks (GNNs) have achieved great success in dealing with non-Euclidean graph-structured data and have been widely deployed in many real-world applications. However, their effectiveness is often jeopardized under class-imbalanced training sets. Most existing studies have analyzed class-imbalanced node classification from a supervised learning perspective, but they do not fully utilize the large number of unlabeled nodes in semi-supervised scenarios. We claim that the supervised signal is just the tip of the iceberg and a large number of unlabeled nodes have not yet been effectiv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.06280","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/2502.06280/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":"2502.06280","created_at":"2026-07-05T10:11:49.957274+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.06280v1","created_at":"2026-07-05T10:11:49.957274+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.06280","created_at":"2026-07-05T10:11:49.957274+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y65VI26FYYCP","created_at":"2026-07-05T10:11:49.957274+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y65VI26FYYCP4LN6","created_at":"2026-07-05T10:11:49.957274+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y65VI26F","created_at":"2026-07-05T10:11:49.957274+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y65VI26FYYCP4LN67D2DMOB2O2","json":"https://pith.science/pith/Y65VI26FYYCP4LN67D2DMOB2O2.json","graph_json":"https://pith.science/api/pith-number/Y65VI26FYYCP4LN67D2DMOB2O2/graph.json","events_json":"https://pith.science/api/pith-number/Y65VI26FYYCP4LN67D2DMOB2O2/events.json","paper":"https://pith.science/paper/Y65VI26F"},"agent_actions":{"view_html":"https://pith.science/pith/Y65VI26FYYCP4LN67D2DMOB2O2","download_json":"https://pith.science/pith/Y65VI26FYYCP4LN67D2DMOB2O2.json","view_paper":"https://pith.science/paper/Y65VI26F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.06280&json=true","fetch_graph":"https://pith.science/api/pith-number/Y65VI26FYYCP4LN67D2DMOB2O2/graph.json","fetch_events":"https://pith.science/api/pith-number/Y65VI26FYYCP4LN67D2DMOB2O2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y65VI26FYYCP4LN67D2DMOB2O2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y65VI26FYYCP4LN67D2DMOB2O2/action/storage_attestation","attest_author":"https://pith.science/pith/Y65VI26FYYCP4LN67D2DMOB2O2/action/author_attestation","sign_citation":"https://pith.science/pith/Y65VI26FYYCP4LN67D2DMOB2O2/action/citation_signature","submit_replication":"https://pith.science/pith/Y65VI26FYYCP4LN67D2DMOB2O2/action/replication_record"}},"created_at":"2026-07-05T10:11:49.957274+00:00","updated_at":"2026-07-05T10:11:49.957274+00:00"}