{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:DGCBDUBM26AX5OY5JREYMTF2OV","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":"cde17e433fdb178fcc5469d6b97cfe6dd8cda7cc4785eb392fe0ae5805d4a8e2","cross_cats_sorted":["cs.SI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-05-29T08:11:40Z","title_canon_sha256":"929872c9aee30b89ede554adf2081a3f7876c4e7e96a9585c2c09a3a5ba3909a"},"schema_version":"1.0","source":{"id":"2105.14244","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.14244","created_at":"2026-07-05T02:44:21Z"},{"alias_kind":"arxiv_version","alias_value":"2105.14244v1","created_at":"2026-07-05T02:44:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.14244","created_at":"2026-07-05T02:44:21Z"},{"alias_kind":"pith_short_12","alias_value":"DGCBDUBM26AX","created_at":"2026-07-05T02:44:21Z"},{"alias_kind":"pith_short_16","alias_value":"DGCBDUBM26AX5OY5","created_at":"2026-07-05T02:44:21Z"},{"alias_kind":"pith_short_8","alias_value":"DGCBDUBM","created_at":"2026-07-05T02:44:21Z"}],"graph_snapshots":[{"event_id":"sha256:9c82176bad22fe8327827ae01561ed4493e2945fd6598717cc762d360b4f2512","target":"graph","created_at":"2026-07-05T02:44:21Z","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/2105.14244/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graphon is a nonparametric model that generates graphs with arbitrary sizes and can be induced from graphs easily. Based on this model, we propose a novel algorithmic framework called \\textit{graphon autoencoder} to build an interpretable and scalable graph generative model. This framework treats observed graphs as induced graphons in functional space and derives their latent representations by an encoder that aggregates Chebshev graphon filters. A linear graphon factorization model works as a decoder, leveraging the latent representations to reconstruct the induced graphons (and the correspon","authors_text":"Dixin Luo, Hongteng Xu, Junzhou Huang, Peilin Zhao","cross_cats":["cs.SI","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-05-29T08:11:40Z","title":"Learning Graphon Autoencoders for Generative Graph Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.14244","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:f524b9303c5c601a38cac7d0038948767cfeea96950fd46f6f2e048d3871057a","target":"record","created_at":"2026-07-05T02:44:21Z","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":"cde17e433fdb178fcc5469d6b97cfe6dd8cda7cc4785eb392fe0ae5805d4a8e2","cross_cats_sorted":["cs.SI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-05-29T08:11:40Z","title_canon_sha256":"929872c9aee30b89ede554adf2081a3f7876c4e7e96a9585c2c09a3a5ba3909a"},"schema_version":"1.0","source":{"id":"2105.14244","kind":"arxiv","version":1}},"canonical_sha256":"198411d02cd7817ebb1d4c49864cba7571a6c94d66eed83e63f4314c6de9ca54","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"198411d02cd7817ebb1d4c49864cba7571a6c94d66eed83e63f4314c6de9ca54","first_computed_at":"2026-07-05T02:44:21.782727Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:44:21.782727Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"p+iuNb1mutxzsIenA/IlEzro4h34xSumf1Qc/7DbALJeoWad6TKn6UaHM8IK79J7o51EvKUkcUHwqDLzexeGAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:44:21.783189Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.14244","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f524b9303c5c601a38cac7d0038948767cfeea96950fd46f6f2e048d3871057a","sha256:9c82176bad22fe8327827ae01561ed4493e2945fd6598717cc762d360b4f2512"],"state_sha256":"c2811384f57f22100bf9cf6eb8994644df37c3d9813e46ccf4650f512b8739fb"}