{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:UE4M6H4SBPCGMQIBFQFMYRFDGO","short_pith_number":"pith:UE4M6H4S","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"},"canonical_sha256":"a138cf1f920bc46641012c0acc44a333971e87c80ad288e3301a9e209bcefc42","source":{"kind":"arxiv","id":"2406.12608","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.12608","created_at":"2026-07-05T09:19:59Z"},{"alias_kind":"arxiv_version","alias_value":"2406.12608v2","created_at":"2026-07-05T09:19:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.12608","created_at":"2026-07-05T09:19:59Z"},{"alias_kind":"pith_short_12","alias_value":"UE4M6H4SBPCG","created_at":"2026-07-05T09:19:59Z"},{"alias_kind":"pith_short_16","alias_value":"UE4M6H4SBPCGMQIB","created_at":"2026-07-05T09:19:59Z"},{"alias_kind":"pith_short_8","alias_value":"UE4M6H4S","created_at":"2026-07-05T09:19:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:UE4M6H4SBPCGMQIBFQFMYRFDGO","target":"record","payload":{"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"},"canonical_sha256":"a138cf1f920bc46641012c0acc44a333971e87c80ad288e3301a9e209bcefc42","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"},"source_kind":"arxiv","source_id":"2406.12608","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:19:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bnyTewRCb5g69rEGwrRi7NKyywq+Ao6COzwPQwuxYRSudib8Xd1L6RgHA+Lo2BsrqEb7/xJsXsSbRXULBRsaAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T02:49:57.892739Z"},"content_sha256":"a204d025eed9bbfe2eb2ca2b515b3d8dc913ecf4a73aba9b9970f67a40456c21","schema_version":"1.0","event_id":"sha256:a204d025eed9bbfe2eb2ca2b515b3d8dc913ecf4a73aba9b9970f67a40456c21"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:UE4M6H4SBPCGMQIBFQFMYRFDGO","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:19:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hZPnXB76XAUCW7zwyjjsEdraITI1mjOpAv/SJ+hlcEczpOs2D9Mri003oRF5CTQwF9pPRSX2kPFkdvr2ResXAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T02:49:57.893207Z"},"content_sha256":"8e98be7857f38b98a5b41bd558837454478197698c2105a6cd69b10f013f7ab3","schema_version":"1.0","event_id":"sha256:8e98be7857f38b98a5b41bd558837454478197698c2105a6cd69b10f013f7ab3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UE4M6H4SBPCGMQIBFQFMYRFDGO/bundle.json","state_url":"https://pith.science/pith/UE4M6H4SBPCGMQIBFQFMYRFDGO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UE4M6H4SBPCGMQIBFQFMYRFDGO/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T02:49:57Z","links":{"resolver":"https://pith.science/pith/UE4M6H4SBPCGMQIBFQFMYRFDGO","bundle":"https://pith.science/pith/UE4M6H4SBPCGMQIBFQFMYRFDGO/bundle.json","state":"https://pith.science/pith/UE4M6H4SBPCGMQIBFQFMYRFDGO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UE4M6H4SBPCGMQIBFQFMYRFDGO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:UE4M6H4SBPCGMQIBFQFMYRFDGO","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0e7093f62113716025fe32e672333e338d8349b76d58da805067814646c38d5f","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-06-18T13:35:25Z","title_canon_sha256":"d21003bf0297c164f5a3f59eadac4a14b02a3b86b75f3f71a2a46047482372b8"},"schema_version":"1.0","source":{"id":"2406.12608","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.12608","created_at":"2026-07-05T09:19:59Z"},{"alias_kind":"arxiv_version","alias_value":"2406.12608v2","created_at":"2026-07-05T09:19:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.12608","created_at":"2026-07-05T09:19:59Z"},{"alias_kind":"pith_short_12","alias_value":"UE4M6H4SBPCG","created_at":"2026-07-05T09:19:59Z"},{"alias_kind":"pith_short_16","alias_value":"UE4M6H4SBPCGMQIB","created_at":"2026-07-05T09:19:59Z"},{"alias_kind":"pith_short_8","alias_value":"UE4M6H4S","created_at":"2026-07-05T09:19:59Z"}],"graph_snapshots":[{"event_id":"sha256:8e98be7857f38b98a5b41bd558837454478197698c2105a6cd69b10f013f7ab3","target":"graph","created_at":"2026-07-05T09:19:59Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2406.12608/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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,","authors_text":"Siliang Tang, Wenqiao Zhang, Yaoke Wang, Yueting Zhuang, Yunfei Li, Yun Zhu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-06-18T13:35:25Z","title":"Bridging Local Details and Global Context in Text-Attributed Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.12608","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a204d025eed9bbfe2eb2ca2b515b3d8dc913ecf4a73aba9b9970f67a40456c21","target":"record","created_at":"2026-07-05T09:19:59Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0e7093f62113716025fe32e672333e338d8349b76d58da805067814646c38d5f","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-06-18T13:35:25Z","title_canon_sha256":"d21003bf0297c164f5a3f59eadac4a14b02a3b86b75f3f71a2a46047482372b8"},"schema_version":"1.0","source":{"id":"2406.12608","kind":"arxiv","version":2}},"canonical_sha256":"a138cf1f920bc46641012c0acc44a333971e87c80ad288e3301a9e209bcefc42","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a138cf1f920bc46641012c0acc44a333971e87c80ad288e3301a9e209bcefc42","first_computed_at":"2026-07-05T09:19:59.561727Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:19:59.561727Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Lnn/IBd5L8a4Us6FmZFhQefG6wPJH+GNbwRj1eUxuzaPBeqKL8HMfv18y50vkGHmLOy1SObo1QIRntQmPtVYCg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:19:59.562150Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.12608","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a204d025eed9bbfe2eb2ca2b515b3d8dc913ecf4a73aba9b9970f67a40456c21","sha256:8e98be7857f38b98a5b41bd558837454478197698c2105a6cd69b10f013f7ab3"],"state_sha256":"78ba61a4ab90e56d6f2240961c552866e02f09aca68b3be203707e374e56e064"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dRmzMbsQZXtvXtxe5hxhI50NaM4oDIHsM+XIhBCj+bpmlb2edN46annBAdEhr7Pk39/01vFH89x/VBdSrMFpDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T02:49:57.896530Z","bundle_sha256":"6a65170776ee22cefe85534d01ce4b1c3061fedbf59fa4e866a7bc81c178d829"}}