{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:ZTUO4OH4WZB4ZJCAQO5LHD33GD","short_pith_number":"pith:ZTUO4OH4","canonical_record":{"source":{"id":"2305.17284","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-26T22:11:38Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"dd1343c42ea32ec1af3b66ff79b0ec466479baa5d306299f00a5bc331a3e8145","abstract_canon_sha256":"195954a893032ad4082cb419044d065bd36f98a2be61cebe74be1d8fb2ebb7a0"},"schema_version":"1.0"},"canonical_sha256":"cce8ee38fcb643cca44083bab38f7b30ea07b0b4a03fb3048fc037d46b9de50d","source":{"kind":"arxiv","id":"2305.17284","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.17284","created_at":"2026-07-05T06:14:26Z"},{"alias_kind":"arxiv_version","alias_value":"2305.17284v1","created_at":"2026-07-05T06:14:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.17284","created_at":"2026-07-05T06:14:26Z"},{"alias_kind":"pith_short_12","alias_value":"ZTUO4OH4WZB4","created_at":"2026-07-05T06:14:26Z"},{"alias_kind":"pith_short_16","alias_value":"ZTUO4OH4WZB4ZJCA","created_at":"2026-07-05T06:14:26Z"},{"alias_kind":"pith_short_8","alias_value":"ZTUO4OH4","created_at":"2026-07-05T06:14:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:ZTUO4OH4WZB4ZJCAQO5LHD33GD","target":"record","payload":{"canonical_record":{"source":{"id":"2305.17284","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-26T22:11:38Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"dd1343c42ea32ec1af3b66ff79b0ec466479baa5d306299f00a5bc331a3e8145","abstract_canon_sha256":"195954a893032ad4082cb419044d065bd36f98a2be61cebe74be1d8fb2ebb7a0"},"schema_version":"1.0"},"canonical_sha256":"cce8ee38fcb643cca44083bab38f7b30ea07b0b4a03fb3048fc037d46b9de50d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:14:26.006808Z","signature_b64":"RlGgfMTNIASVQMaKx7D57GAvqaRcF9ryOg/PxHkWkB/SbZON+wXAozrLA0hu5zkP27lqGzozL2Xq+QsK2/08Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cce8ee38fcb643cca44083bab38f7b30ea07b0b4a03fb3048fc037d46b9de50d","last_reissued_at":"2026-07-05T06:14:26.006340Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:14:26.006340Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.17284","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:14:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oWd/xDvnYOZBJ5pbKkTZiXsjCRZANMFV6Hs12uer5yRXMNMmeplyb3T0NXJkUREDRq7rYLF3W0+HLSBDnNrrAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T16:31:07.907409Z"},"content_sha256":"971662d6bd21fde288f398f5e74b22b5824ab0a4e48f154044f37eb5f71f0de8","schema_version":"1.0","event_id":"sha256:971662d6bd21fde288f398f5e74b22b5824ab0a4e48f154044f37eb5f71f0de8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:ZTUO4OH4WZB4ZJCAQO5LHD33GD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GC-Flow: A Graph-Based Flow Network for Effective Clustering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Farzaneh Mirzazadeh, Jie Chen, Tianchun Wang, Xiang Zhang","submitted_at":"2023-05-26T22:11:38Z","abstract_excerpt":"Graph convolutional networks (GCNs) are \\emph{discriminative models} that directly model the class posterior $p(y|\\mathbf{x})$ for semi-supervised classification of graph data. While being effective, as a representation learning approach, the node representations extracted from a GCN often miss useful information for effective clustering, because the objectives are different. In this work, we design normalizing flows that replace GCN layers, leading to a \\emph{generative model} that models both the class conditional likelihood $p(\\mathbf{x}|y)$ and the class prior $p(y)$. The resulting neural "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.17284","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2305.17284/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:14:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mV26q+X0ZSFQDU2hQU1rEus7ECRUpSgsGUhZLhaJbGlzLTLynZA/RZlECaBpH2kUSzAI08Pwq3bAZkkkZSIhBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T16:31:07.908324Z"},"content_sha256":"7d857571e93c2335d6f71c919ead720eef26d02eda6cfcbeafca04a0f09e8c31","schema_version":"1.0","event_id":"sha256:7d857571e93c2335d6f71c919ead720eef26d02eda6cfcbeafca04a0f09e8c31"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZTUO4OH4WZB4ZJCAQO5LHD33GD/bundle.json","state_url":"https://pith.science/pith/ZTUO4OH4WZB4ZJCAQO5LHD33GD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZTUO4OH4WZB4ZJCAQO5LHD33GD/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-14T16:31:07Z","links":{"resolver":"https://pith.science/pith/ZTUO4OH4WZB4ZJCAQO5LHD33GD","bundle":"https://pith.science/pith/ZTUO4OH4WZB4ZJCAQO5LHD33GD/bundle.json","state":"https://pith.science/pith/ZTUO4OH4WZB4ZJCAQO5LHD33GD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZTUO4OH4WZB4ZJCAQO5LHD33GD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ZTUO4OH4WZB4ZJCAQO5LHD33GD","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":"195954a893032ad4082cb419044d065bd36f98a2be61cebe74be1d8fb2ebb7a0","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-26T22:11:38Z","title_canon_sha256":"dd1343c42ea32ec1af3b66ff79b0ec466479baa5d306299f00a5bc331a3e8145"},"schema_version":"1.0","source":{"id":"2305.17284","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.17284","created_at":"2026-07-05T06:14:26Z"},{"alias_kind":"arxiv_version","alias_value":"2305.17284v1","created_at":"2026-07-05T06:14:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.17284","created_at":"2026-07-05T06:14:26Z"},{"alias_kind":"pith_short_12","alias_value":"ZTUO4OH4WZB4","created_at":"2026-07-05T06:14:26Z"},{"alias_kind":"pith_short_16","alias_value":"ZTUO4OH4WZB4ZJCA","created_at":"2026-07-05T06:14:26Z"},{"alias_kind":"pith_short_8","alias_value":"ZTUO4OH4","created_at":"2026-07-05T06:14:26Z"}],"graph_snapshots":[{"event_id":"sha256:7d857571e93c2335d6f71c919ead720eef26d02eda6cfcbeafca04a0f09e8c31","target":"graph","created_at":"2026-07-05T06:14:26Z","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/2305.17284/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph convolutional networks (GCNs) are \\emph{discriminative models} that directly model the class posterior $p(y|\\mathbf{x})$ for semi-supervised classification of graph data. While being effective, as a representation learning approach, the node representations extracted from a GCN often miss useful information for effective clustering, because the objectives are different. In this work, we design normalizing flows that replace GCN layers, leading to a \\emph{generative model} that models both the class conditional likelihood $p(\\mathbf{x}|y)$ and the class prior $p(y)$. The resulting neural ","authors_text":"Farzaneh Mirzazadeh, Jie Chen, Tianchun Wang, Xiang Zhang","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-26T22:11:38Z","title":"GC-Flow: A Graph-Based Flow Network for Effective Clustering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.17284","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":"sha256:971662d6bd21fde288f398f5e74b22b5824ab0a4e48f154044f37eb5f71f0de8","target":"record","created_at":"2026-07-05T06:14:26Z","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":"195954a893032ad4082cb419044d065bd36f98a2be61cebe74be1d8fb2ebb7a0","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-26T22:11:38Z","title_canon_sha256":"dd1343c42ea32ec1af3b66ff79b0ec466479baa5d306299f00a5bc331a3e8145"},"schema_version":"1.0","source":{"id":"2305.17284","kind":"arxiv","version":1}},"canonical_sha256":"cce8ee38fcb643cca44083bab38f7b30ea07b0b4a03fb3048fc037d46b9de50d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cce8ee38fcb643cca44083bab38f7b30ea07b0b4a03fb3048fc037d46b9de50d","first_computed_at":"2026-07-05T06:14:26.006340Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:14:26.006340Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RlGgfMTNIASVQMaKx7D57GAvqaRcF9ryOg/PxHkWkB/SbZON+wXAozrLA0hu5zkP27lqGzozL2Xq+QsK2/08Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:14:26.006808Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.17284","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:971662d6bd21fde288f398f5e74b22b5824ab0a4e48f154044f37eb5f71f0de8","sha256:7d857571e93c2335d6f71c919ead720eef26d02eda6cfcbeafca04a0f09e8c31"],"state_sha256":"5490c9127110317efc0d3c69b049ab13df743e629bfa05b5313fd6b442bbcddd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NYp2AwLuattMHiKtR9WsuPXjidwdnZxiXsmIZ7agqKTosojoBzrsNjqzbyBUp1Y6pbTMEzbYKT9rZ01k3F0NAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T16:31:07.914455Z","bundle_sha256":"0686da5e94d8df1d77002cd807238f76de95b91608a44c226587f46c4d67201f"}}