{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ETDDBKC7QY52HHAB3UZI6O7FQL","short_pith_number":"pith:ETDDBKC7","schema_version":"1.0","canonical_sha256":"24c630a85f863ba39c01dd328f3be582c78c706999d17de7c70e0f24b95c877d","source":{"kind":"arxiv","id":"2411.16787","version":1},"attestation_state":"computed","paper":{"title":"Contrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Jianbin Li, Keqin Lib, Tao Meng, Wei Ai, Yingying Wei, Yuntao Shou, Ze Wang","submitted_at":"2024-11-25T08:35:55Z","abstract_excerpt":"Graph contrastive learning has been successfully applied in text classification due to its remarkable ability for self-supervised node representation learning. However, explicit graph augmentations may lead to a loss of semantics in the contrastive views. Secondly, existing methods tend to overlook edge features and the varying significance of node features during multi-graph learning. Moreover, the contrastive loss suffer from false negatives. To address these limitations, we propose a novel method of contrastive multi-graph learning with neighbor hierarchical sifting for semi-supervised text"},"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":"2411.16787","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-25T08:35:55Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"8d459ae1059507abe4e3478d9776ccc35e9f5abd71fbc7c26fd0158a2a5e8617","abstract_canon_sha256":"c102abbbf51e6b7148473ce0f91e8d05a483a0813e53b1923f0e70338413b1f4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:40:45.505503Z","signature_b64":"RfyistRNRTHnXg3Urz0Gl811Sxz8A298+9AgkO1qkU7SN6ROb5Q4FlkobVXM4kow8up4BqprjdA3oqQ7c4/MCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24c630a85f863ba39c01dd328f3be582c78c706999d17de7c70e0f24b95c877d","last_reissued_at":"2026-07-05T09:40:45.505014Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:40:45.505014Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Contrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Jianbin Li, Keqin Lib, Tao Meng, Wei Ai, Yingying Wei, Yuntao Shou, Ze Wang","submitted_at":"2024-11-25T08:35:55Z","abstract_excerpt":"Graph contrastive learning has been successfully applied in text classification due to its remarkable ability for self-supervised node representation learning. However, explicit graph augmentations may lead to a loss of semantics in the contrastive views. Secondly, existing methods tend to overlook edge features and the varying significance of node features during multi-graph learning. Moreover, the contrastive loss suffer from false negatives. To address these limitations, we propose a novel method of contrastive multi-graph learning with neighbor hierarchical sifting for semi-supervised text"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.16787","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/2411.16787/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":"2411.16787","created_at":"2026-07-05T09:40:45.505077+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.16787v1","created_at":"2026-07-05T09:40:45.505077+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.16787","created_at":"2026-07-05T09:40:45.505077+00:00"},{"alias_kind":"pith_short_12","alias_value":"ETDDBKC7QY52","created_at":"2026-07-05T09:40:45.505077+00:00"},{"alias_kind":"pith_short_16","alias_value":"ETDDBKC7QY52HHAB","created_at":"2026-07-05T09:40:45.505077+00:00"},{"alias_kind":"pith_short_8","alias_value":"ETDDBKC7","created_at":"2026-07-05T09:40:45.505077+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.02935","citing_title":"Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ETDDBKC7QY52HHAB3UZI6O7FQL","json":"https://pith.science/pith/ETDDBKC7QY52HHAB3UZI6O7FQL.json","graph_json":"https://pith.science/api/pith-number/ETDDBKC7QY52HHAB3UZI6O7FQL/graph.json","events_json":"https://pith.science/api/pith-number/ETDDBKC7QY52HHAB3UZI6O7FQL/events.json","paper":"https://pith.science/paper/ETDDBKC7"},"agent_actions":{"view_html":"https://pith.science/pith/ETDDBKC7QY52HHAB3UZI6O7FQL","download_json":"https://pith.science/pith/ETDDBKC7QY52HHAB3UZI6O7FQL.json","view_paper":"https://pith.science/paper/ETDDBKC7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.16787&json=true","fetch_graph":"https://pith.science/api/pith-number/ETDDBKC7QY52HHAB3UZI6O7FQL/graph.json","fetch_events":"https://pith.science/api/pith-number/ETDDBKC7QY52HHAB3UZI6O7FQL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ETDDBKC7QY52HHAB3UZI6O7FQL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ETDDBKC7QY52HHAB3UZI6O7FQL/action/storage_attestation","attest_author":"https://pith.science/pith/ETDDBKC7QY52HHAB3UZI6O7FQL/action/author_attestation","sign_citation":"https://pith.science/pith/ETDDBKC7QY52HHAB3UZI6O7FQL/action/citation_signature","submit_replication":"https://pith.science/pith/ETDDBKC7QY52HHAB3UZI6O7FQL/action/replication_record"}},"created_at":"2026-07-05T09:40:45.505077+00:00","updated_at":"2026-07-05T09:40:45.505077+00:00"}