{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZPNLXJEB5KLLS2P3ORQRQD6YND","short_pith_number":"pith:ZPNLXJEB","schema_version":"1.0","canonical_sha256":"cbdabba481ea96b969fb7461180fd868dbf19267979cfa1f6dde7939bf7116ed","source":{"kind":"arxiv","id":"2502.00829","version":2},"attestation_state":"computed","paper":{"title":"When Do LLMs Help With Node Classification? A Comprehensive Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.LG","authors_text":"Caihua Shan, Fangzhou Ge, Hong Cheng, Xiangguo Sun, Xixi Wu, Yifei Shen, Yizhu Jiao","submitted_at":"2025-02-02T15:56:05Z","abstract_excerpt":"Node classification is a fundamental task in graph analysis, with broad applications across various fields. Recent breakthroughs in Large Language Models (LLMs) have enabled LLM-based approaches for this task. Although many studies demonstrate the impressive performance of LLM-based methods, the lack of clear design guidelines may hinder their practical application. In this work, we aim to establish such guidelines through a fair and systematic comparison of these algorithms. As a first step, we developed LLMNodeBed, a comprehensive codebase and testbed for node classification using LLMs. It i"},"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":"2502.00829","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-02T15:56:05Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"bfcca9a42616ec3311cf7519d6384e3e7aac4ad9d3c982b4e73888b6ccd429da","abstract_canon_sha256":"fee49d33298fbc08d88a53470e3dbd015da96d7bf2aa7776fe9f504acda5fd93"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:05:39.358515Z","signature_b64":"PW94lWkdxgImdZX5yI2eWHSg5yEkyv6Z/WG25qsWdHvuwBU+s92iB23p2vV8ejZlRtvbQP9PVzsc0MxpdJXxCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cbdabba481ea96b969fb7461180fd868dbf19267979cfa1f6dde7939bf7116ed","last_reissued_at":"2026-07-05T11:05:39.358019Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:05:39.358019Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"When Do LLMs Help With Node Classification? A Comprehensive Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.LG","authors_text":"Caihua Shan, Fangzhou Ge, Hong Cheng, Xiangguo Sun, Xixi Wu, Yifei Shen, Yizhu Jiao","submitted_at":"2025-02-02T15:56:05Z","abstract_excerpt":"Node classification is a fundamental task in graph analysis, with broad applications across various fields. Recent breakthroughs in Large Language Models (LLMs) have enabled LLM-based approaches for this task. Although many studies demonstrate the impressive performance of LLM-based methods, the lack of clear design guidelines may hinder their practical application. In this work, we aim to establish such guidelines through a fair and systematic comparison of these algorithms. As a first step, we developed LLMNodeBed, a comprehensive codebase and testbed for node classification using LLMs. It i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.00829","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/2502.00829/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":"2502.00829","created_at":"2026-07-05T11:05:39.358078+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.00829v2","created_at":"2026-07-05T11:05:39.358078+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.00829","created_at":"2026-07-05T11:05:39.358078+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZPNLXJEB5KLL","created_at":"2026-07-05T11:05:39.358078+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZPNLXJEB5KLLS2P3","created_at":"2026-07-05T11:05:39.358078+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZPNLXJEB","created_at":"2026-07-05T11:05:39.358078+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11898","citing_title":"GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11583","citing_title":"Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2606.01873","citing_title":"G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2606.29773","citing_title":"GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2511.16767","citing_title":"When Structure Doesn't Help: LLMs Do Not Read Text-Attributed Graphs as Effectively as We Expected","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17271","citing_title":"HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZPNLXJEB5KLLS2P3ORQRQD6YND","json":"https://pith.science/pith/ZPNLXJEB5KLLS2P3ORQRQD6YND.json","graph_json":"https://pith.science/api/pith-number/ZPNLXJEB5KLLS2P3ORQRQD6YND/graph.json","events_json":"https://pith.science/api/pith-number/ZPNLXJEB5KLLS2P3ORQRQD6YND/events.json","paper":"https://pith.science/paper/ZPNLXJEB"},"agent_actions":{"view_html":"https://pith.science/pith/ZPNLXJEB5KLLS2P3ORQRQD6YND","download_json":"https://pith.science/pith/ZPNLXJEB5KLLS2P3ORQRQD6YND.json","view_paper":"https://pith.science/paper/ZPNLXJEB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.00829&json=true","fetch_graph":"https://pith.science/api/pith-number/ZPNLXJEB5KLLS2P3ORQRQD6YND/graph.json","fetch_events":"https://pith.science/api/pith-number/ZPNLXJEB5KLLS2P3ORQRQD6YND/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZPNLXJEB5KLLS2P3ORQRQD6YND/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZPNLXJEB5KLLS2P3ORQRQD6YND/action/storage_attestation","attest_author":"https://pith.science/pith/ZPNLXJEB5KLLS2P3ORQRQD6YND/action/author_attestation","sign_citation":"https://pith.science/pith/ZPNLXJEB5KLLS2P3ORQRQD6YND/action/citation_signature","submit_replication":"https://pith.science/pith/ZPNLXJEB5KLLS2P3ORQRQD6YND/action/replication_record"}},"created_at":"2026-07-05T11:05:39.358078+00:00","updated_at":"2026-07-05T11:05:39.358078+00:00"}