{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7EZ7NZU6FJB6VVYKEEZMU4LV4K","short_pith_number":"pith:7EZ7NZU6","schema_version":"1.0","canonical_sha256":"f933f6e69e2a43ead70a2132ca7175e28ed6af82cc47cd2de931a9fe8442cf3e","source":{"kind":"arxiv","id":"2402.05952","version":1},"attestation_state":"computed","paper":{"title":"Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Chenghao Liu, Jianling Sun, Qiheng Mao, Zemin Liu, Zhuo Li","submitted_at":"2024-02-04T05:51:14Z","abstract_excerpt":"The integration of Large Language Models (LLMs) with Graph Representation Learning (GRL) marks a significant evolution in analyzing complex data structures. This collaboration harnesses the sophisticated linguistic capabilities of LLMs to improve the contextual understanding and adaptability of graph models, thereby broadening the scope and potential of GRL. Despite a growing body of research dedicated to integrating LLMs into the graph domain, a comprehensive review that deeply analyzes the core components and operations within these models is notably lacking. Our survey fills this gap by pro"},"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":"2402.05952","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-04T05:51:14Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"bd8e443bdce627cd9e2ab1f3d453e2d30b6d26531445a875981dfb742b5c39a1","abstract_canon_sha256":"0c293715cfe1d9ec1a1f6399ca2f8e4c18c472f0a581634b0041e9bee806d9d3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:43:15.856641Z","signature_b64":"bzzCHWs5A7Qgjy/DTXIMt1/fg6dDFWkcjsLcAnlLcKuB2rAg5hevswnQWsUkwJLlixsnCGHOY5LgX2p+BKxsDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f933f6e69e2a43ead70a2132ca7175e28ed6af82cc47cd2de931a9fe8442cf3e","last_reissued_at":"2026-07-05T07:43:15.856103Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:43:15.856103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Chenghao Liu, Jianling Sun, Qiheng Mao, Zemin Liu, Zhuo Li","submitted_at":"2024-02-04T05:51:14Z","abstract_excerpt":"The integration of Large Language Models (LLMs) with Graph Representation Learning (GRL) marks a significant evolution in analyzing complex data structures. This collaboration harnesses the sophisticated linguistic capabilities of LLMs to improve the contextual understanding and adaptability of graph models, thereby broadening the scope and potential of GRL. Despite a growing body of research dedicated to integrating LLMs into the graph domain, a comprehensive review that deeply analyzes the core components and operations within these models is notably lacking. Our survey fills this gap by pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.05952","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/2402.05952/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":"2402.05952","created_at":"2026-07-05T07:43:15.856163+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.05952v1","created_at":"2026-07-05T07:43:15.856163+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.05952","created_at":"2026-07-05T07:43:15.856163+00:00"},{"alias_kind":"pith_short_12","alias_value":"7EZ7NZU6FJB6","created_at":"2026-07-05T07:43:15.856163+00:00"},{"alias_kind":"pith_short_16","alias_value":"7EZ7NZU6FJB6VVYK","created_at":"2026-07-05T07:43:15.856163+00:00"},{"alias_kind":"pith_short_8","alias_value":"7EZ7NZU6","created_at":"2026-07-05T07:43:15.856163+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.03527","citing_title":"Multilevel Analysis of Cryptocurrency News using RAG Approach with Fine-Tuned Mistral Large Language Model","ref_index":27,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7EZ7NZU6FJB6VVYKEEZMU4LV4K","json":"https://pith.science/pith/7EZ7NZU6FJB6VVYKEEZMU4LV4K.json","graph_json":"https://pith.science/api/pith-number/7EZ7NZU6FJB6VVYKEEZMU4LV4K/graph.json","events_json":"https://pith.science/api/pith-number/7EZ7NZU6FJB6VVYKEEZMU4LV4K/events.json","paper":"https://pith.science/paper/7EZ7NZU6"},"agent_actions":{"view_html":"https://pith.science/pith/7EZ7NZU6FJB6VVYKEEZMU4LV4K","download_json":"https://pith.science/pith/7EZ7NZU6FJB6VVYKEEZMU4LV4K.json","view_paper":"https://pith.science/paper/7EZ7NZU6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.05952&json=true","fetch_graph":"https://pith.science/api/pith-number/7EZ7NZU6FJB6VVYKEEZMU4LV4K/graph.json","fetch_events":"https://pith.science/api/pith-number/7EZ7NZU6FJB6VVYKEEZMU4LV4K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7EZ7NZU6FJB6VVYKEEZMU4LV4K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7EZ7NZU6FJB6VVYKEEZMU4LV4K/action/storage_attestation","attest_author":"https://pith.science/pith/7EZ7NZU6FJB6VVYKEEZMU4LV4K/action/author_attestation","sign_citation":"https://pith.science/pith/7EZ7NZU6FJB6VVYKEEZMU4LV4K/action/citation_signature","submit_replication":"https://pith.science/pith/7EZ7NZU6FJB6VVYKEEZMU4LV4K/action/replication_record"}},"created_at":"2026-07-05T07:43:15.856163+00:00","updated_at":"2026-07-05T07:43:15.856163+00:00"}