{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:VRJZIC4AWRZ25QFJLS5IJY2ZVX","short_pith_number":"pith:VRJZIC4A","schema_version":"1.0","canonical_sha256":"ac53940b80b473aec0a95cba84e359ade6f1b340fd87499f38af61753d660931","source":{"kind":"arxiv","id":"2307.03393","version":4},"attestation_state":"computed","paper":{"title":"Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Dawei Yin, Haitao Mao, Hang Li, Hongzhi Wen, Hui Liu, Jiliang Tang, Shuaiqiang Wang, Wei Jin, Wenqi Fan, Xiaochi Wei, Zhikai Chen","submitted_at":"2023-07-07T05:31:31Z","abstract_excerpt":"Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we a"},"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":"2307.03393","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-07T05:31:31Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"41180c2c4ab2c35c44bf877209003d809f9229738612200502ad1c9dd6718113","abstract_canon_sha256":"278e0d8da835b5521434c3cd3522d402f1796dcbc5967d761d5f3fc47f8d8dff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:33:52.786685Z","signature_b64":"ijCSXSYa59ZVh1ZcIZrye5KDpsEtKblAz1MQTKVQsJkIQGJZBvWqxjYsEJ1c81uDEVWIJ1itX1LoE9UZSo+pDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac53940b80b473aec0a95cba84e359ade6f1b340fd87499f38af61753d660931","last_reissued_at":"2026-07-05T07:33:52.786182Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:33:52.786182Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Dawei Yin, Haitao Mao, Hang Li, Hongzhi Wen, Hui Liu, Jiliang Tang, Shuaiqiang Wang, Wei Jin, Wenqi Fan, Xiaochi Wei, Zhikai Chen","submitted_at":"2023-07-07T05:31:31Z","abstract_excerpt":"Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.03393","kind":"arxiv","version":4},"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/2307.03393/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":"2307.03393","created_at":"2026-07-05T07:33:52.786245+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.03393v4","created_at":"2026-07-05T07:33:52.786245+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.03393","created_at":"2026-07-05T07:33:52.786245+00:00"},{"alias_kind":"pith_short_12","alias_value":"VRJZIC4AWRZ2","created_at":"2026-07-05T07:33:52.786245+00:00"},{"alias_kind":"pith_short_16","alias_value":"VRJZIC4AWRZ25QFJ","created_at":"2026-07-05T07:33:52.786245+00:00"},{"alias_kind":"pith_short_8","alias_value":"VRJZIC4A","created_at":"2026-07-05T07:33:52.786245+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.17667","citing_title":"Handling Feature Heterogeneity with Learnable Graph Patches","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11663","citing_title":"Probabilistic Salary Prediction with Graph Attention Networks and a Mixture Density Network","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2605.27913","citing_title":"Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VRJZIC4AWRZ25QFJLS5IJY2ZVX","json":"https://pith.science/pith/VRJZIC4AWRZ25QFJLS5IJY2ZVX.json","graph_json":"https://pith.science/api/pith-number/VRJZIC4AWRZ25QFJLS5IJY2ZVX/graph.json","events_json":"https://pith.science/api/pith-number/VRJZIC4AWRZ25QFJLS5IJY2ZVX/events.json","paper":"https://pith.science/paper/VRJZIC4A"},"agent_actions":{"view_html":"https://pith.science/pith/VRJZIC4AWRZ25QFJLS5IJY2ZVX","download_json":"https://pith.science/pith/VRJZIC4AWRZ25QFJLS5IJY2ZVX.json","view_paper":"https://pith.science/paper/VRJZIC4A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.03393&json=true","fetch_graph":"https://pith.science/api/pith-number/VRJZIC4AWRZ25QFJLS5IJY2ZVX/graph.json","fetch_events":"https://pith.science/api/pith-number/VRJZIC4AWRZ25QFJLS5IJY2ZVX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VRJZIC4AWRZ25QFJLS5IJY2ZVX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VRJZIC4AWRZ25QFJLS5IJY2ZVX/action/storage_attestation","attest_author":"https://pith.science/pith/VRJZIC4AWRZ25QFJLS5IJY2ZVX/action/author_attestation","sign_citation":"https://pith.science/pith/VRJZIC4AWRZ25QFJLS5IJY2ZVX/action/citation_signature","submit_replication":"https://pith.science/pith/VRJZIC4AWRZ25QFJLS5IJY2ZVX/action/replication_record"}},"created_at":"2026-07-05T07:33:52.786245+00:00","updated_at":"2026-07-05T07:33:52.786245+00:00"}