{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:6DBNBFRVBHJK355EVZMWDOCFDJ","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":"c6b5521a5dd0327a3adce4a808d2ed43cda9a3e0a581028686038d37e809eb6c","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2024-07-22T07:24:21Z","title_canon_sha256":"05dcb940e90208eecb739ecfd58d676b4707b27b85df9e40eabbaa9beec0c8d8"},"schema_version":"1.0","source":{"id":"2407.15431","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.15431","created_at":"2026-07-05T08:46:43Z"},{"alias_kind":"arxiv_version","alias_value":"2407.15431v1","created_at":"2026-07-05T08:46:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.15431","created_at":"2026-07-05T08:46:43Z"},{"alias_kind":"pith_short_12","alias_value":"6DBNBFRVBHJK","created_at":"2026-07-05T08:46:43Z"},{"alias_kind":"pith_short_16","alias_value":"6DBNBFRVBHJK355E","created_at":"2026-07-05T08:46:43Z"},{"alias_kind":"pith_short_8","alias_value":"6DBNBFRV","created_at":"2026-07-05T08:46:43Z"}],"graph_snapshots":[{"event_id":"sha256:602279c2b7dc12e9b6248bfc8a2afbc3089a8e95c690155f3dcbf35db185dfd7","target":"graph","created_at":"2026-07-05T08:46:43Z","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/2407.15431/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The text-attributed graph (TAG) is one kind of important real-world graph-structured data with each node associated with raw texts. For TAGs, traditional few-shot node classification methods directly conduct training on the pre-processed node features and do not consider the raw texts. The performance is highly dependent on the choice of the feature pre-processing method. In this paper, we propose P2TAG, a framework designed for few-shot node classification on TAGs with graph pre-training and prompting. P2TAG first pre-trains the language model (LM) and graph neural network (GNN) on TAGs with ","authors_text":"Beining Yang, Chenhui Zhang, Evgeny Kharlamov, Huanjing Zhao, Jie Tang, Junyu Ren, Shu Zhao, Yukuo Cen, Yuxiao Dong","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2024-07-22T07:24:21Z","title":"Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.15431","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:ce78a03409d09f55d3e7939d63cd4f68e19b58cdf486e7c0d6651bd54f4479b0","target":"record","created_at":"2026-07-05T08:46:43Z","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":"c6b5521a5dd0327a3adce4a808d2ed43cda9a3e0a581028686038d37e809eb6c","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2024-07-22T07:24:21Z","title_canon_sha256":"05dcb940e90208eecb739ecfd58d676b4707b27b85df9e40eabbaa9beec0c8d8"},"schema_version":"1.0","source":{"id":"2407.15431","kind":"arxiv","version":1}},"canonical_sha256":"f0c2d0963509d2adf7a4ae5961b8451a4766ee73df0bf99a6059809e74ee90f8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f0c2d0963509d2adf7a4ae5961b8451a4766ee73df0bf99a6059809e74ee90f8","first_computed_at":"2026-07-05T08:46:43.775597Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:46:43.775597Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5CaEL4svJ+EFJxy9/CSOqbaHVPf3UM4WK8cWWW+kZE7Y33/kxWHoaIJ5+Y61ZCgQvPkGMwWzMAsTuqq8FmZ3BA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:46:43.776066Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.15431","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ce78a03409d09f55d3e7939d63cd4f68e19b58cdf486e7c0d6651bd54f4479b0","sha256:602279c2b7dc12e9b6248bfc8a2afbc3089a8e95c690155f3dcbf35db185dfd7"],"state_sha256":"d9a436494d02a4dad3b5f1a0324cbd6217283d7a6d5e19dc1a285429e1457ebc"}