{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DLZEQE7XVLYJON2HLOC4TVII2N","short_pith_number":"pith:DLZEQE7X","schema_version":"1.0","canonical_sha256":"1af24813f7aaf09737475b85c9d508d351573e226ed276054cd092427ffc6bea","source":{"kind":"arxiv","id":"2404.14809","version":2},"attestation_state":"computed","paper":{"title":"A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DB"],"primary_cat":"cs.CL","authors_text":"Wenbo Shang, Xin Huang","submitted_at":"2024-04-23T07:39:24Z","abstract_excerpt":"A graph is a fundamental data model to represent various entities and their complex relationships in society and nature, such as social networks, transportation networks, and financial networks. Recently, large language models (LLMs) have showcased a strong generalization ability to handle various natural language processing tasks to answer users' arbitrary questions and generate specific-domain content. Compared with graph learning models, LLMs enjoy superior advantages in addressing the challenges of generalizing graph tasks by eliminating the need for training graph learning models and redu"},"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":"2404.14809","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-04-23T07:39:24Z","cross_cats_sorted":["cs.AI","cs.DB"],"title_canon_sha256":"b209839bfe553326e81e3eb538ff5cbda99cd6199d8ceb21c34920f96d17a448","abstract_canon_sha256":"15b5218729f5e5406da8aea3f7986f84028a7eca4b1d507b9b3309ffd1dbc062"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:31:38.507557Z","signature_b64":"RAfEyIXCQGRjHKQhs+xLBibsIQ/vz0tQXeb3IkHeh2FzWNAwwzXPOxsrjqfC6oyMgGk7t3yYWGXeFUI7VwffAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1af24813f7aaf09737475b85c9d508d351573e226ed276054cd092427ffc6bea","last_reissued_at":"2026-07-05T11:31:38.507127Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:31:38.507127Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DB"],"primary_cat":"cs.CL","authors_text":"Wenbo Shang, Xin Huang","submitted_at":"2024-04-23T07:39:24Z","abstract_excerpt":"A graph is a fundamental data model to represent various entities and their complex relationships in society and nature, such as social networks, transportation networks, and financial networks. Recently, large language models (LLMs) have showcased a strong generalization ability to handle various natural language processing tasks to answer users' arbitrary questions and generate specific-domain content. Compared with graph learning models, LLMs enjoy superior advantages in addressing the challenges of generalizing graph tasks by eliminating the need for training graph learning models and redu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.14809","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/2404.14809/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":"2404.14809","created_at":"2026-07-05T11:31:38.507181+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.14809v2","created_at":"2026-07-05T11:31:38.507181+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.14809","created_at":"2026-07-05T11:31:38.507181+00:00"},{"alias_kind":"pith_short_12","alias_value":"DLZEQE7XVLYJ","created_at":"2026-07-05T11:31:38.507181+00:00"},{"alias_kind":"pith_short_16","alias_value":"DLZEQE7XVLYJON2H","created_at":"2026-07-05T11:31:38.507181+00:00"},{"alias_kind":"pith_short_8","alias_value":"DLZEQE7X","created_at":"2026-07-05T11:31:38.507181+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":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DLZEQE7XVLYJON2HLOC4TVII2N","json":"https://pith.science/pith/DLZEQE7XVLYJON2HLOC4TVII2N.json","graph_json":"https://pith.science/api/pith-number/DLZEQE7XVLYJON2HLOC4TVII2N/graph.json","events_json":"https://pith.science/api/pith-number/DLZEQE7XVLYJON2HLOC4TVII2N/events.json","paper":"https://pith.science/paper/DLZEQE7X"},"agent_actions":{"view_html":"https://pith.science/pith/DLZEQE7XVLYJON2HLOC4TVII2N","download_json":"https://pith.science/pith/DLZEQE7XVLYJON2HLOC4TVII2N.json","view_paper":"https://pith.science/paper/DLZEQE7X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.14809&json=true","fetch_graph":"https://pith.science/api/pith-number/DLZEQE7XVLYJON2HLOC4TVII2N/graph.json","fetch_events":"https://pith.science/api/pith-number/DLZEQE7XVLYJON2HLOC4TVII2N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DLZEQE7XVLYJON2HLOC4TVII2N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DLZEQE7XVLYJON2HLOC4TVII2N/action/storage_attestation","attest_author":"https://pith.science/pith/DLZEQE7XVLYJON2HLOC4TVII2N/action/author_attestation","sign_citation":"https://pith.science/pith/DLZEQE7XVLYJON2HLOC4TVII2N/action/citation_signature","submit_replication":"https://pith.science/pith/DLZEQE7XVLYJON2HLOC4TVII2N/action/replication_record"}},"created_at":"2026-07-05T11:31:38.507181+00:00","updated_at":"2026-07-05T11:31:38.507181+00:00"}