{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UE4M6H4SBPCGMQIBFQFMYRFDGO","short_pith_number":"pith:UE4M6H4S","schema_version":"1.0","canonical_sha256":"a138cf1f920bc46641012c0acc44a333971e87c80ad288e3301a9e209bcefc42","source":{"kind":"arxiv","id":"2406.12608","version":2},"attestation_state":"computed","paper":{"title":"Bridging Local Details and Global Context in Text-Attributed Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Siliang Tang, Wenqiao Zhang, Yaoke Wang, Yueting Zhuang, Yunfei Li, Yun Zhu","submitted_at":"2024-06-18T13:35:25Z","abstract_excerpt":"Representation learning on text-attributed graphs (TAGs) is vital for real-world applications, as they combine semantic textual and contextual structural information. Research in this field generally consist of two main perspectives: local-level encoding and global-level aggregating, respectively refer to textual node information unification (e.g., using Language Models) and structure-augmented modeling (e.g., using Graph Neural Networks). Most existing works focus on combining different information levels but overlook the interconnections, i.e., the contextual textual information among nodes,"},"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":"2406.12608","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-06-18T13:35:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d21003bf0297c164f5a3f59eadac4a14b02a3b86b75f3f71a2a46047482372b8","abstract_canon_sha256":"0e7093f62113716025fe32e672333e338d8349b76d58da805067814646c38d5f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:19:59.562150Z","signature_b64":"Lnn/IBd5L8a4Us6FmZFhQefG6wPJH+GNbwRj1eUxuzaPBeqKL8HMfv18y50vkGHmLOy1SObo1QIRntQmPtVYCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a138cf1f920bc46641012c0acc44a333971e87c80ad288e3301a9e209bcefc42","last_reissued_at":"2026-07-05T09:19:59.561727Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:19:59.561727Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bridging Local Details and Global Context in Text-Attributed Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Siliang Tang, Wenqiao Zhang, Yaoke Wang, Yueting Zhuang, Yunfei Li, Yun Zhu","submitted_at":"2024-06-18T13:35:25Z","abstract_excerpt":"Representation learning on text-attributed graphs (TAGs) is vital for real-world applications, as they combine semantic textual and contextual structural information. Research in this field generally consist of two main perspectives: local-level encoding and global-level aggregating, respectively refer to textual node information unification (e.g., using Language Models) and structure-augmented modeling (e.g., using Graph Neural Networks). Most existing works focus on combining different information levels but overlook the interconnections, i.e., the contextual textual information among nodes,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.12608","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/2406.12608/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":"2406.12608","created_at":"2026-07-05T09:19:59.561783+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.12608v2","created_at":"2026-07-05T09:19:59.561783+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.12608","created_at":"2026-07-05T09:19:59.561783+00:00"},{"alias_kind":"pith_short_12","alias_value":"UE4M6H4SBPCG","created_at":"2026-07-05T09:19:59.561783+00:00"},{"alias_kind":"pith_short_16","alias_value":"UE4M6H4SBPCGMQIB","created_at":"2026-07-05T09:19:59.561783+00:00"},{"alias_kind":"pith_short_8","alias_value":"UE4M6H4S","created_at":"2026-07-05T09:19:59.561783+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2504.02343","citing_title":"Toward General and Robust LLM-enhanced Text-attributed Graph Learning","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UE4M6H4SBPCGMQIBFQFMYRFDGO","json":"https://pith.science/pith/UE4M6H4SBPCGMQIBFQFMYRFDGO.json","graph_json":"https://pith.science/api/pith-number/UE4M6H4SBPCGMQIBFQFMYRFDGO/graph.json","events_json":"https://pith.science/api/pith-number/UE4M6H4SBPCGMQIBFQFMYRFDGO/events.json","paper":"https://pith.science/paper/UE4M6H4S"},"agent_actions":{"view_html":"https://pith.science/pith/UE4M6H4SBPCGMQIBFQFMYRFDGO","download_json":"https://pith.science/pith/UE4M6H4SBPCGMQIBFQFMYRFDGO.json","view_paper":"https://pith.science/paper/UE4M6H4S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.12608&json=true","fetch_graph":"https://pith.science/api/pith-number/UE4M6H4SBPCGMQIBFQFMYRFDGO/graph.json","fetch_events":"https://pith.science/api/pith-number/UE4M6H4SBPCGMQIBFQFMYRFDGO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UE4M6H4SBPCGMQIBFQFMYRFDGO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UE4M6H4SBPCGMQIBFQFMYRFDGO/action/storage_attestation","attest_author":"https://pith.science/pith/UE4M6H4SBPCGMQIBFQFMYRFDGO/action/author_attestation","sign_citation":"https://pith.science/pith/UE4M6H4SBPCGMQIBFQFMYRFDGO/action/citation_signature","submit_replication":"https://pith.science/pith/UE4M6H4SBPCGMQIBFQFMYRFDGO/action/replication_record"}},"created_at":"2026-07-05T09:19:59.561783+00:00","updated_at":"2026-07-05T09:19:59.561783+00:00"}