{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:2XER6LFBELSRMPMJVCX6O4DPAL","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":"e4322e026b2b788014fe2e663a93d973a69a78d6a9fe0035c8a52f11efd326fa","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-22T00:23:37Z","title_canon_sha256":"85db636904ddad86cb11d95cdd90e91dae858e76f0a10f3a2cb1644f2f3b76c2"},"schema_version":"1.0","source":{"id":"2206.10781","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.10781","created_at":"2026-07-05T04:33:55Z"},{"alias_kind":"arxiv_version","alias_value":"2206.10781v1","created_at":"2026-07-05T04:33:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.10781","created_at":"2026-07-05T04:33:55Z"},{"alias_kind":"pith_short_12","alias_value":"2XER6LFBELSR","created_at":"2026-07-05T04:33:55Z"},{"alias_kind":"pith_short_16","alias_value":"2XER6LFBELSRMPMJ","created_at":"2026-07-05T04:33:55Z"},{"alias_kind":"pith_short_8","alias_value":"2XER6LFB","created_at":"2026-07-05T04:33:55Z"}],"graph_snapshots":[{"event_id":"sha256:f6c43063d58a2e716382630730367a8fee051555d1958d258a67be825b62ef8e","target":"graph","created_at":"2026-07-05T04:33:55Z","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/2206.10781/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Can we combine heterogenous graph structure with text to learn high-quality semantic and behavioural representations? Graph neural networks (GNN)s encode numerical node attributes and graph structure to achieve impressive performance in a variety of supervised learning tasks. Current GNN approaches are challenged by textual features, which typically need to be encoded to a numerical vector before provided to the GNN that may incur some information loss. In this paper, we put forth an efficient and effective framework termed language model GNN (LM-GNN) to jointly train large-scale language mode","authors_text":"Belinda Zeng, Da Zheng, George Karypis, Houyu Zhang, Jun Ma, Trishul Chilimbi, Vassilis N. Ioannidis, Xiang Song, Yi Xu","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-22T00:23:37Z","title":"Efficient and effective training of language and graph neural network models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.10781","kind":"arxiv","version":1},"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":"sha256:88512bcd3b6a9bb4f45ac287d05b1bf288ae7fd3d489208f2753d8d590a9d966","target":"record","created_at":"2026-07-05T04:33:55Z","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":"e4322e026b2b788014fe2e663a93d973a69a78d6a9fe0035c8a52f11efd326fa","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-22T00:23:37Z","title_canon_sha256":"85db636904ddad86cb11d95cdd90e91dae858e76f0a10f3a2cb1644f2f3b76c2"},"schema_version":"1.0","source":{"id":"2206.10781","kind":"arxiv","version":1}},"canonical_sha256":"d5c91f2ca122e5163d89a8afe7706f02ea4446b9b331ea909f96e04518b7824b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d5c91f2ca122e5163d89a8afe7706f02ea4446b9b331ea909f96e04518b7824b","first_computed_at":"2026-07-05T04:33:55.376198Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:33:55.376198Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MG6n84Clpu2KaKi4Qj5QUj3U2ReC44mZFxQSJa+PMVIt0113B/t5Mf+NCNhMrN3MLk3ZTHB6nbCwA18rTCdHDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:33:55.376650Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.10781","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:88512bcd3b6a9bb4f45ac287d05b1bf288ae7fd3d489208f2753d8d590a9d966","sha256:f6c43063d58a2e716382630730367a8fee051555d1958d258a67be825b62ef8e"],"state_sha256":"8ee27f392685d4f2e7b5d8a1706601586c9333985367192c83f55741c4c3e259"}