{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:UFZWTRFCNG76X6U5WRD5R7FAMC","short_pith_number":"pith:UFZWTRFC","schema_version":"1.0","canonical_sha256":"a17369c4a269bfebfa9db447d8fca0609e19059f20e40d9c32821b4bb9cc7e84","source":{"kind":"arxiv","id":"2210.14709","version":2},"attestation_state":"computed","paper":{"title":"Learning on Large-scale Text-attributed Graphs via Variational Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chaozhuo Li, Hao Yan, Jianan Zhao, Jian Tang, Meng Qu, Qian Liu, Rui Li, Xing Xie","submitted_at":"2022-10-26T13:40:57Z","abstract_excerpt":"This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. An ideal solution for such a problem would be integrating both the text and graph structure information with large language models and graph neural networks (GNNs). However, the problem becomes very challenging when graphs are large due to the high computational complexity brought by training large language models and GNNs together. In this paper, we propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language lea"},"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":"2210.14709","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-26T13:40:57Z","cross_cats_sorted":[],"title_canon_sha256":"d01d823316d25ba0f318aeb49cc9e7c17c731b8f945e5075ead7248da61a9207","abstract_canon_sha256":"7f0bbfab7b2d8f1232850f1c253c4c077cdddbe0c175d31d6a75eceadb44129b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:47:04.557775Z","signature_b64":"VdkxN2zW/A67KSwu+SLiABA20lywgMQ3+wljyyzy+1xC8x4qr0aYY41vM0nYpkAtRB/noxr/xGk2/5rrBHgkCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a17369c4a269bfebfa9db447d8fca0609e19059f20e40d9c32821b4bb9cc7e84","last_reissued_at":"2026-07-05T05:47:04.557273Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:47:04.557273Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning on Large-scale Text-attributed Graphs via Variational Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chaozhuo Li, Hao Yan, Jianan Zhao, Jian Tang, Meng Qu, Qian Liu, Rui Li, Xing Xie","submitted_at":"2022-10-26T13:40:57Z","abstract_excerpt":"This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. An ideal solution for such a problem would be integrating both the text and graph structure information with large language models and graph neural networks (GNNs). However, the problem becomes very challenging when graphs are large due to the high computational complexity brought by training large language models and GNNs together. In this paper, we propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language lea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.14709","kind":"arxiv","version":2},"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/2210.14709/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":"2210.14709","created_at":"2026-07-05T05:47:04.557338+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.14709v2","created_at":"2026-07-05T05:47:04.557338+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.14709","created_at":"2026-07-05T05:47:04.557338+00:00"},{"alias_kind":"pith_short_12","alias_value":"UFZWTRFCNG76","created_at":"2026-07-05T05:47:04.557338+00:00"},{"alias_kind":"pith_short_16","alias_value":"UFZWTRFCNG76X6U5","created_at":"2026-07-05T05:47:04.557338+00:00"},{"alias_kind":"pith_short_8","alias_value":"UFZWTRFC","created_at":"2026-07-05T05:47:04.557338+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":10,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11583","citing_title":"Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2606.32016","citing_title":"FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning","ref_index":176,"is_internal_anchor":false},{"citing_arxiv_id":"2605.26040","citing_title":"L2IR: Revealing Latent Intent in Graph Fraud Detection","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2605.27913","citing_title":"Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2605.30247","citing_title":"OOD-GraphLLM: Graph Large Language Model for Out-of-Distribution Generalized Drug Synergy Prediction","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2504.02343","citing_title":"Toward General and Robust LLM-enhanced Text-attributed Graph Learning","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2511.02135","citing_title":"Graph-Based Alternatives to LLMs for Human Simulation","ref_index":99,"is_internal_anchor":false},{"citing_arxiv_id":"2603.01410","citing_title":"GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning","ref_index":57,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15951","citing_title":"Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17411","citing_title":"DuConTE: Dual-Granularity Text Encoder with Topology-Constrained Attention for Text-attributed Graphs","ref_index":34,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UFZWTRFCNG76X6U5WRD5R7FAMC","json":"https://pith.science/pith/UFZWTRFCNG76X6U5WRD5R7FAMC.json","graph_json":"https://pith.science/api/pith-number/UFZWTRFCNG76X6U5WRD5R7FAMC/graph.json","events_json":"https://pith.science/api/pith-number/UFZWTRFCNG76X6U5WRD5R7FAMC/events.json","paper":"https://pith.science/paper/UFZWTRFC"},"agent_actions":{"view_html":"https://pith.science/pith/UFZWTRFCNG76X6U5WRD5R7FAMC","download_json":"https://pith.science/pith/UFZWTRFCNG76X6U5WRD5R7FAMC.json","view_paper":"https://pith.science/paper/UFZWTRFC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.14709&json=true","fetch_graph":"https://pith.science/api/pith-number/UFZWTRFCNG76X6U5WRD5R7FAMC/graph.json","fetch_events":"https://pith.science/api/pith-number/UFZWTRFCNG76X6U5WRD5R7FAMC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UFZWTRFCNG76X6U5WRD5R7FAMC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UFZWTRFCNG76X6U5WRD5R7FAMC/action/storage_attestation","attest_author":"https://pith.science/pith/UFZWTRFCNG76X6U5WRD5R7FAMC/action/author_attestation","sign_citation":"https://pith.science/pith/UFZWTRFCNG76X6U5WRD5R7FAMC/action/citation_signature","submit_replication":"https://pith.science/pith/UFZWTRFCNG76X6U5WRD5R7FAMC/action/replication_record"}},"created_at":"2026-07-05T05:47:04.557338+00:00","updated_at":"2026-07-05T05:47:04.557338+00:00"}