{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:TKHNAPVGM2KSJQSAU5MBBHCUIZ","short_pith_number":"pith:TKHNAPVG","canonical_record":{"source":{"id":"2410.04263","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-05T18:52:54Z","cross_cats_sorted":[],"title_canon_sha256":"016d33ff5724a7e2c56810266b0294be97c059ecd5585c66aff8a8a5a45e166f","abstract_canon_sha256":"9b1d6b2d2557abc4bcb1ed8224fa32c8dff1d03e5b6007c92c035d502ce307da"},"schema_version":"1.0"},"canonical_sha256":"9a8ed03ea6669524c240a758109c54465b8f7be64e9317167eb280f485e8ce43","source":{"kind":"arxiv","id":"2410.04263","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.04263","created_at":"2026-07-05T11:21:36Z"},{"alias_kind":"arxiv_version","alias_value":"2410.04263v3","created_at":"2026-07-05T11:21:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.04263","created_at":"2026-07-05T11:21:36Z"},{"alias_kind":"pith_short_12","alias_value":"TKHNAPVGM2KS","created_at":"2026-07-05T11:21:36Z"},{"alias_kind":"pith_short_16","alias_value":"TKHNAPVGM2KSJQSA","created_at":"2026-07-05T11:21:36Z"},{"alias_kind":"pith_short_8","alias_value":"TKHNAPVG","created_at":"2026-07-05T11:21:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:TKHNAPVGM2KSJQSAU5MBBHCUIZ","target":"record","payload":{"canonical_record":{"source":{"id":"2410.04263","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-05T18:52:54Z","cross_cats_sorted":[],"title_canon_sha256":"016d33ff5724a7e2c56810266b0294be97c059ecd5585c66aff8a8a5a45e166f","abstract_canon_sha256":"9b1d6b2d2557abc4bcb1ed8224fa32c8dff1d03e5b6007c92c035d502ce307da"},"schema_version":"1.0"},"canonical_sha256":"9a8ed03ea6669524c240a758109c54465b8f7be64e9317167eb280f485e8ce43","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:36.552145Z","signature_b64":"2FWDAYF+MBsVpxc2yA0YF2erBVvYDGLorgXSbu5tJgbYUz8S4MLAmnNXWohOd9Af8E0PJCVApQ6pzoP3DMibCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9a8ed03ea6669524c240a758109c54465b8f7be64e9317167eb280f485e8ce43","last_reissued_at":"2026-07-05T11:21:36.551613Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:36.551613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.04263","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:21:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"juvdQiOoZCd7RswMYy7nuxz0Zqjnf2Ntdx+xXMdQWsaQCFXobHgGrCtqtL88WlpYV5KPcWEmNe5kRUxRE7nyDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T22:15:04.269975Z"},"content_sha256":"99db5337d746e6feb08bc0ae4940761586e391486638cead50a4287ffc5f154b","schema_version":"1.0","event_id":"sha256:99db5337d746e6feb08bc0ae4940761586e391486638cead50a4287ffc5f154b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:TKHNAPVGM2KSJQSAU5MBBHCUIZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DeFoG: Discrete Flow Matching for Graph Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dorina Thanou, Manuel Madeira, Pascal Frossard, Yiming Qin","submitted_at":"2024-10-05T18:52:54Z","abstract_excerpt":"Graph generative models are essential across diverse scientific domains by capturing complex distributions over relational data. Among them, graph diffusion models achieve superior performance but face inefficient sampling and limited flexibility due to the tight coupling between training and sampling stages. We introduce DeFoG, a novel graph generative framework that disentangles sampling from training, enabling a broader design space for more effective and efficient model optimization. DeFoG employs a discrete flow-matching formulation that respects the inherent symmetries of graphs. We theo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.04263","kind":"arxiv","version":3},"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/2410.04263/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":"2026-07-05T11:21:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LJ78cpXlS1wr14j/ngc6ZKcaYUhIo9q+plW2IXzCQH1cvLS88Fipf/kiuG4d+7aG5qONH1F2FsAYJYgurOe4Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T22:15:04.270515Z"},"content_sha256":"7945285c8c5112f2286ab21b29d32182aea0820ac0b87bb8e370f56e213a00a9","schema_version":"1.0","event_id":"sha256:7945285c8c5112f2286ab21b29d32182aea0820ac0b87bb8e370f56e213a00a9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TKHNAPVGM2KSJQSAU5MBBHCUIZ/bundle.json","state_url":"https://pith.science/pith/TKHNAPVGM2KSJQSAU5MBBHCUIZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TKHNAPVGM2KSJQSAU5MBBHCUIZ/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":"2026-08-01T22:15:04Z","links":{"resolver":"https://pith.science/pith/TKHNAPVGM2KSJQSAU5MBBHCUIZ","bundle":"https://pith.science/pith/TKHNAPVGM2KSJQSAU5MBBHCUIZ/bundle.json","state":"https://pith.science/pith/TKHNAPVGM2KSJQSAU5MBBHCUIZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TKHNAPVGM2KSJQSAU5MBBHCUIZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:TKHNAPVGM2KSJQSAU5MBBHCUIZ","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":"9b1d6b2d2557abc4bcb1ed8224fa32c8dff1d03e5b6007c92c035d502ce307da","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-05T18:52:54Z","title_canon_sha256":"016d33ff5724a7e2c56810266b0294be97c059ecd5585c66aff8a8a5a45e166f"},"schema_version":"1.0","source":{"id":"2410.04263","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.04263","created_at":"2026-07-05T11:21:36Z"},{"alias_kind":"arxiv_version","alias_value":"2410.04263v3","created_at":"2026-07-05T11:21:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.04263","created_at":"2026-07-05T11:21:36Z"},{"alias_kind":"pith_short_12","alias_value":"TKHNAPVGM2KS","created_at":"2026-07-05T11:21:36Z"},{"alias_kind":"pith_short_16","alias_value":"TKHNAPVGM2KSJQSA","created_at":"2026-07-05T11:21:36Z"},{"alias_kind":"pith_short_8","alias_value":"TKHNAPVG","created_at":"2026-07-05T11:21:36Z"}],"graph_snapshots":[{"event_id":"sha256:7945285c8c5112f2286ab21b29d32182aea0820ac0b87bb8e370f56e213a00a9","target":"graph","created_at":"2026-07-05T11:21:36Z","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/2410.04263/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph generative models are essential across diverse scientific domains by capturing complex distributions over relational data. Among them, graph diffusion models achieve superior performance but face inefficient sampling and limited flexibility due to the tight coupling between training and sampling stages. We introduce DeFoG, a novel graph generative framework that disentangles sampling from training, enabling a broader design space for more effective and efficient model optimization. DeFoG employs a discrete flow-matching formulation that respects the inherent symmetries of graphs. We theo","authors_text":"Dorina Thanou, Manuel Madeira, Pascal Frossard, Yiming Qin","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-05T18:52:54Z","title":"DeFoG: Discrete Flow Matching for Graph Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.04263","kind":"arxiv","version":3},"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":"sha256:99db5337d746e6feb08bc0ae4940761586e391486638cead50a4287ffc5f154b","target":"record","created_at":"2026-07-05T11:21:36Z","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":"9b1d6b2d2557abc4bcb1ed8224fa32c8dff1d03e5b6007c92c035d502ce307da","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-05T18:52:54Z","title_canon_sha256":"016d33ff5724a7e2c56810266b0294be97c059ecd5585c66aff8a8a5a45e166f"},"schema_version":"1.0","source":{"id":"2410.04263","kind":"arxiv","version":3}},"canonical_sha256":"9a8ed03ea6669524c240a758109c54465b8f7be64e9317167eb280f485e8ce43","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9a8ed03ea6669524c240a758109c54465b8f7be64e9317167eb280f485e8ce43","first_computed_at":"2026-07-05T11:21:36.551613Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:21:36.551613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2FWDAYF+MBsVpxc2yA0YF2erBVvYDGLorgXSbu5tJgbYUz8S4MLAmnNXWohOd9Af8E0PJCVApQ6pzoP3DMibCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:21:36.552145Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.04263","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:99db5337d746e6feb08bc0ae4940761586e391486638cead50a4287ffc5f154b","sha256:7945285c8c5112f2286ab21b29d32182aea0820ac0b87bb8e370f56e213a00a9"],"state_sha256":"38d906331c4cab1333e784bbd46352955227b2587d54e39ffe3d3309acbf2bb4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aqXxG95gUcfHYSOOP4hFypv8GyvrMIu3LuqLLAIXDBhFnH6A+vySTLpA+ReBGCbXeh3R0yb9fadihVfHXT0vAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T22:15:04.274267Z","bundle_sha256":"5feb017483991a18350cc7c5fa0fb0965b7ab65eacecc7bd4bcb339342c76c77"}}