{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:WV4DFYHRLNYCJUUGOMZBEO6552","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":"64b006ef991ef7db795d1e51c5de57537fb954f34b24b465d690cd07d4fddf22","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-03T14:07:41Z","title_canon_sha256":"cfce76e13de6cfef3f522e386ba19888a90cedaed4886f98e97efb2d62580f6c"},"schema_version":"1.0","source":{"id":"2407.03125","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.03125","created_at":"2026-07-05T08:41:05Z"},{"alias_kind":"arxiv_version","alias_value":"2407.03125v2","created_at":"2026-07-05T08:41:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.03125","created_at":"2026-07-05T08:41:05Z"},{"alias_kind":"pith_short_12","alias_value":"WV4DFYHRLNYC","created_at":"2026-07-05T08:41:05Z"},{"alias_kind":"pith_short_16","alias_value":"WV4DFYHRLNYCJUUG","created_at":"2026-07-05T08:41:05Z"},{"alias_kind":"pith_short_8","alias_value":"WV4DFYHR","created_at":"2026-07-05T08:41:05Z"}],"graph_snapshots":[{"event_id":"sha256:2e205e06b3715a300ccd74bd472b0f80a1e06f089edfcd29f8c8475cf31ab21d","target":"graph","created_at":"2026-07-05T08:41:05Z","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/2407.03125/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures. Notably, Graph Neural Networks (GNNs), i.e. neural network architectures designed for learning graph representations, have become a popular paradigm. With these models being usually characterized by intuition-driven design or highly intricate components, placing them within the theoretical analysis framework to distill the core concepts, helps understand the key principles that drive the functionality better and guide further development. Given this surge in interest, this ","authors_text":"Defu Lian, Enhong Chen, Hao Wang, Hong Xie, Jie Wang, Menglin Yang, Min Zhou, Muhan Zhang, Yu Huang, Zhen Wang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-03T14:07:41Z","title":"Foundations and Frontiers of Graph Learning Theory"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.03125","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":"sha256:44b0040dc2306e820e7ece606a2d35673722cc588ad893bfb44ec0192162845a","target":"record","created_at":"2026-07-05T08:41:05Z","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":"64b006ef991ef7db795d1e51c5de57537fb954f34b24b465d690cd07d4fddf22","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-03T14:07:41Z","title_canon_sha256":"cfce76e13de6cfef3f522e386ba19888a90cedaed4886f98e97efb2d62580f6c"},"schema_version":"1.0","source":{"id":"2407.03125","kind":"arxiv","version":2}},"canonical_sha256":"b57832e0f15b7024d2867332123bddeeb3b3b8039bbae9f6722aa8db290c8b50","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b57832e0f15b7024d2867332123bddeeb3b3b8039bbae9f6722aa8db290c8b50","first_computed_at":"2026-07-05T08:41:05.503499Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:41:05.503499Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4N75QJOeT0Nd06K7HuFMei60pYHwiFELeuNxv67983sTupnZzeZuv2k2vJ0XUuEg0L38CPanap6Y8TLt19tABw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:41:05.504057Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.03125","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:44b0040dc2306e820e7ece606a2d35673722cc588ad893bfb44ec0192162845a","sha256:2e205e06b3715a300ccd74bd472b0f80a1e06f089edfcd29f8c8475cf31ab21d"],"state_sha256":"d6c3e3955b8ec3125cf8dc463b3b2f156828a02a612c8149c4d9751a83b57708"}