{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:HOXOFYV5YPLXE2FV4XMEOTTPYK","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":"60591865c0df236e8ada4781e3b9f79f312aee29ae2836215124872cb9d9cf0d","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-08-09T23:25:37Z","title_canon_sha256":"66d4d4cb9488c9688e607c6465068a343f260696fe40ea24d885ec3b83cbe33e"},"schema_version":"1.0","source":{"id":"2408.15257","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.15257","created_at":"2026-07-05T09:00:09Z"},{"alias_kind":"arxiv_version","alias_value":"2408.15257v1","created_at":"2026-07-05T09:00:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.15257","created_at":"2026-07-05T09:00:09Z"},{"alias_kind":"pith_short_12","alias_value":"HOXOFYV5YPLX","created_at":"2026-07-05T09:00:09Z"},{"alias_kind":"pith_short_16","alias_value":"HOXOFYV5YPLXE2FV","created_at":"2026-07-05T09:00:09Z"},{"alias_kind":"pith_short_8","alias_value":"HOXOFYV5","created_at":"2026-07-05T09:00:09Z"}],"graph_snapshots":[{"event_id":"sha256:7993ecfda77c3d499ffd5726c10b7736d2c48f58ea01ed9c224b8b1d867ed4ee","target":"graph","created_at":"2026-07-05T09:00:09Z","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/2408.15257/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the field of natural language processing, text classification, as a basic task, has important research value and application prospects. Traditional text classification methods usually rely on feature representations such as the bag of words model or TF-IDF, which overlook the semantic connections between words and make it challenging to grasp the deep structural details of the text. Recently, GNNs have proven to be a valuable asset for text classification tasks, thanks to their capability to handle non-Euclidean data efficiently. However, the existing text classification methods based on GN","authors_text":"Dan Sun, Erdi Gao, Haohao Xia, Haowei Yang, Yuanjing Zhu, Yuhan Ma","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-08-09T23:25:37Z","title":"Text classification optimization algorithm based on graph neural network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.15257","kind":"arxiv","version":1},"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:ee1a60eaca16b821af5467cb14869d66b95c8f9c7e835c59587f7a731355fab5","target":"record","created_at":"2026-07-05T09:00:09Z","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":"60591865c0df236e8ada4781e3b9f79f312aee29ae2836215124872cb9d9cf0d","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-08-09T23:25:37Z","title_canon_sha256":"66d4d4cb9488c9688e607c6465068a343f260696fe40ea24d885ec3b83cbe33e"},"schema_version":"1.0","source":{"id":"2408.15257","kind":"arxiv","version":1}},"canonical_sha256":"3baee2e2bdc3d77268b5e5d8474e6fc28a24a8abd5db663cae59c6fce5cae9f5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3baee2e2bdc3d77268b5e5d8474e6fc28a24a8abd5db663cae59c6fce5cae9f5","first_computed_at":"2026-07-05T09:00:09.112361Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:00:09.112361Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mKjTrrQrdy3R2LH2BzgRC8wLiDL46LtERiLi+9M7NnezhDg9jPF0jBfQP02icm0u81hkMmIIdhsFvbV+TTS/Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:00:09.112861Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.15257","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ee1a60eaca16b821af5467cb14869d66b95c8f9c7e835c59587f7a731355fab5","sha256:7993ecfda77c3d499ffd5726c10b7736d2c48f58ea01ed9c224b8b1d867ed4ee"],"state_sha256":"406c6469f2137d5d25be1b5dacb08930521f266a5ccd6265296ef5b4081d64de"}