{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:IVB2VBRTG4KI67JVZZZS7C6B2F","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":"84cdd83fea955276109feb12fddcab8ac74969aa4719ce2beaeb37373be3e062","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-02T04:52:52Z","title_canon_sha256":"0723de5b0623804e4e0db673c08397b2d5cdb61493600da6104b85bc325b391c"},"schema_version":"1.0","source":{"id":"2402.01143","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.01143","created_at":"2026-07-05T08:44:35Z"},{"alias_kind":"arxiv_version","alias_value":"2402.01143v2","created_at":"2026-07-05T08:44:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.01143","created_at":"2026-07-05T08:44:35Z"},{"alias_kind":"pith_short_12","alias_value":"IVB2VBRTG4KI","created_at":"2026-07-05T08:44:35Z"},{"alias_kind":"pith_short_16","alias_value":"IVB2VBRTG4KI67JV","created_at":"2026-07-05T08:44:35Z"},{"alias_kind":"pith_short_8","alias_value":"IVB2VBRT","created_at":"2026-07-05T08:44:35Z"}],"graph_snapshots":[{"event_id":"sha256:e34b4fa724090b78393bbb84b396b34d9ace095c7cf29b67eff62b2200c5aeae","target":"graph","created_at":"2026-07-05T08:44:35Z","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/2402.01143/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The (variational) graph auto-encoder is widely used to learn representations for graph-structured data. However, the formation of real-world graphs is a complicated and heterogeneous process influenced by latent factors. Existing encoders are fundamentally holistic, neglecting the entanglement of latent factors. This reduces the effectiveness of graph analysis tasks, while also making it more difficult to explain the learned representations. As a result, learning disentangled graph representations with the (variational) graph auto-encoder poses significant challenges and remains largely unexpl","authors_text":"Chuanhou Gao, Di Fan","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-02T04:52:52Z","title":"Learning Network Representations with Disentangled Graph Auto-Encoder"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.01143","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":"sha256:54da4b06f3d935ac877da2c82d04bd09e873b69f7e01e723eae06b3fbf096402","target":"record","created_at":"2026-07-05T08:44:35Z","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":"84cdd83fea955276109feb12fddcab8ac74969aa4719ce2beaeb37373be3e062","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-02T04:52:52Z","title_canon_sha256":"0723de5b0623804e4e0db673c08397b2d5cdb61493600da6104b85bc325b391c"},"schema_version":"1.0","source":{"id":"2402.01143","kind":"arxiv","version":2}},"canonical_sha256":"4543aa863337148f7d35ce732f8bc1d153facf8cfbaa882d66b3ae8acfe524d5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4543aa863337148f7d35ce732f8bc1d153facf8cfbaa882d66b3ae8acfe524d5","first_computed_at":"2026-07-05T08:44:35.203785Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:44:35.203785Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1qUgtV6QOpsTAuU3MRTdfabuAh89MvBTiR61uwfOtu7sf31JHtWPb8daFdZDgxwhd2bW5EEg3gylxPgQY+aQCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:44:35.204166Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.01143","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:54da4b06f3d935ac877da2c82d04bd09e873b69f7e01e723eae06b3fbf096402","sha256:e34b4fa724090b78393bbb84b396b34d9ace095c7cf29b67eff62b2200c5aeae"],"state_sha256":"5549e97aba0a96dc58639531aa3216022f2f6ec05032b3c678465f09b97df301"}