{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:T5SYJMUI4PTSNWMNGE4TA472NV","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":"211f36e810bc2b355abbea2899e4831a3d76c3356e91eca0fc32606f80661d04","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-07T16:35:56Z","title_canon_sha256":"3e04f36b7b4de40090cd855737140fd0b18ae8e10322f01d60771fa23eda6adf"},"schema_version":"1.0","source":{"id":"1906.03220","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.03220","created_at":"2026-07-05T02:18:13Z"},{"alias_kind":"arxiv_version","alias_value":"1906.03220v2","created_at":"2026-07-05T02:18:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.03220","created_at":"2026-07-05T02:18:13Z"},{"alias_kind":"pith_short_12","alias_value":"T5SYJMUI4PTS","created_at":"2026-07-05T02:18:13Z"},{"alias_kind":"pith_short_16","alias_value":"T5SYJMUI4PTSNWMN","created_at":"2026-07-05T02:18:13Z"},{"alias_kind":"pith_short_8","alias_value":"T5SYJMUI","created_at":"2026-07-05T02:18:13Z"}],"graph_snapshots":[{"event_id":"sha256:f29d82131a0a67e0756d8195e42a5f2087f13e8d839412a38b19d06620d0cb29","target":"graph","created_at":"2026-07-05T02:18:13Z","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/1906.03220/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As a new approach to train generative models, \\emph{generative adversarial networks} (GANs) have achieved considerable success in image generation. This framework has also recently been applied to data with graph structures. We propose labeled-graph generative adversarial networks (LGGAN) to train deep generative models for graph-structured data with node labels. We test the approach on various types of graph datasets, such as collections of citation networks and protein graphs. Experiment results show that our model can generate diverse labeled graphs that match the structural characteristics","authors_text":"Bert Huang, Shuangfei Fan","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-07T16:35:56Z","title":"Labeled Graph Generative Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.03220","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:e228b33737d3baf8c772f9bcc44739b2745792af1104c1808ba6cdd606817b86","target":"record","created_at":"2026-07-05T02:18:13Z","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":"211f36e810bc2b355abbea2899e4831a3d76c3356e91eca0fc32606f80661d04","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-07T16:35:56Z","title_canon_sha256":"3e04f36b7b4de40090cd855737140fd0b18ae8e10322f01d60771fa23eda6adf"},"schema_version":"1.0","source":{"id":"1906.03220","kind":"arxiv","version":2}},"canonical_sha256":"9f6584b288e3e726d98d31393073fa6d42042ae5cd4e2e0f066f9cecba10ddb3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9f6584b288e3e726d98d31393073fa6d42042ae5cd4e2e0f066f9cecba10ddb3","first_computed_at":"2026-07-05T02:18:13.716876Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:18:13.716876Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dFt+zjhV/1Exdn5Q82eF76yAV4tC+ViprV0FjCQZo9C930WpPotHZjZr0TERUHA4S6kakXs9i6GQ/SZpMVEmCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:18:13.717393Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.03220","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e228b33737d3baf8c772f9bcc44739b2745792af1104c1808ba6cdd606817b86","sha256:f29d82131a0a67e0756d8195e42a5f2087f13e8d839412a38b19d06620d0cb29"],"state_sha256":"e6d7b2cdf27c6b5ba6ddfc095b8f8bbdbec4c87967eed880195be2ede8c829f1"}