{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:FBCS7QSIJFINQB5YU6BLPQILYJ","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":"4634881a67cab293abafd45ce5ce6bdb4ff8b0eca9d2a8ee3514bfd9380e2721","cross_cats_sorted":["cs.AI","cs.GR","cs.SI"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-07T23:50:00Z","title_canon_sha256":"ae62319006ac3cfbc5a3b82c04032fd61b551ba1b1920042b6622e904ba3fd2e"},"schema_version":"1.0","source":{"id":"2402.05322","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.05322","created_at":"2026-07-05T07:42:48Z"},{"alias_kind":"arxiv_version","alias_value":"2402.05322v1","created_at":"2026-07-05T07:42:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.05322","created_at":"2026-07-05T07:42:48Z"},{"alias_kind":"pith_short_12","alias_value":"FBCS7QSIJFIN","created_at":"2026-07-05T07:42:48Z"},{"alias_kind":"pith_short_16","alias_value":"FBCS7QSIJFINQB5Y","created_at":"2026-07-05T07:42:48Z"},{"alias_kind":"pith_short_8","alias_value":"FBCS7QSI","created_at":"2026-07-05T07:42:48Z"}],"graph_snapshots":[{"event_id":"sha256:2e0d1274a69849f12635936e4d0100000949b988c58c72e391b2c728ccf46dfa","target":"graph","created_at":"2026-07-05T07:42:48Z","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/2402.05322/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multimodal data pervades various domains, including healthcare, social media, and transportation, where multimodal graphs play a pivotal role. Machine learning on multimodal graphs, referred to as multimodal graph learning (MGL), is essential for successful artificial intelligence (AI) applications. The burgeoning research in this field encompasses diverse graph data types and modalities, learning techniques, and application scenarios. This survey paper conducts a comparative analysis of existing works in multimodal graph learning, elucidating how multimodal learning is achieved across differe","authors_text":"Ciyuan Peng, Feng Xia, Jiayuan He","cross_cats":["cs.AI","cs.GR","cs.SI"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-07T23:50:00Z","title":"Learning on Multimodal Graphs: A Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.05322","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:c67173397b735c3e61160a3f8edf55ed032cfcf6f6fa3d0d1f9c956d162392fb","target":"record","created_at":"2026-07-05T07:42:48Z","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":"4634881a67cab293abafd45ce5ce6bdb4ff8b0eca9d2a8ee3514bfd9380e2721","cross_cats_sorted":["cs.AI","cs.GR","cs.SI"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-07T23:50:00Z","title_canon_sha256":"ae62319006ac3cfbc5a3b82c04032fd61b551ba1b1920042b6622e904ba3fd2e"},"schema_version":"1.0","source":{"id":"2402.05322","kind":"arxiv","version":1}},"canonical_sha256":"28452fc2484950d807b8a782b7c10bc24e482bd576e8a107587f60590eb9a254","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"28452fc2484950d807b8a782b7c10bc24e482bd576e8a107587f60590eb9a254","first_computed_at":"2026-07-05T07:42:48.093134Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:42:48.093134Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iVYcelaulHFNvTa/XeAcEP336MibrgAuIwO6TiNUGRsx0pVOg1LzNNaumAQFflTguclB0OIVEuykTlJiRKIEDw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:42:48.093708Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.05322","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c67173397b735c3e61160a3f8edf55ed032cfcf6f6fa3d0d1f9c956d162392fb","sha256:2e0d1274a69849f12635936e4d0100000949b988c58c72e391b2c728ccf46dfa"],"state_sha256":"db7909d25070293c6d7e1d8766c749e194dc5f71b1be47082330665e7a520b99"}