{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:UWWHJCJHNRSPG4S5DBS72INPPS","short_pith_number":"pith:UWWHJCJH","canonical_record":{"source":{"id":"2307.01646","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-04T10:58:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bd781d0540441d3641c45fa946f677f8ff0e3c0a1e4438b5fcf1c4798ff13e6a","abstract_canon_sha256":"36919784a0760df134c8e26356f5b7277c6c6806db3a54da9ae0e96a656f7885"},"schema_version":"1.0"},"canonical_sha256":"a5ac7489276c64f3725d1865fd21af7cbc15fb9d2268714d1fac6cfe0d0200ce","source":{"kind":"arxiv","id":"2307.01646","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.01646","created_at":"2026-07-05T08:34:04Z"},{"alias_kind":"arxiv_version","alias_value":"2307.01646v4","created_at":"2026-07-05T08:34:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.01646","created_at":"2026-07-05T08:34:04Z"},{"alias_kind":"pith_short_12","alias_value":"UWWHJCJHNRSP","created_at":"2026-07-05T08:34:04Z"},{"alias_kind":"pith_short_16","alias_value":"UWWHJCJHNRSPG4S5","created_at":"2026-07-05T08:34:04Z"},{"alias_kind":"pith_short_8","alias_value":"UWWHJCJH","created_at":"2026-07-05T08:34:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:UWWHJCJHNRSPG4S5DBS72INPPS","target":"record","payload":{"canonical_record":{"source":{"id":"2307.01646","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-04T10:58:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bd781d0540441d3641c45fa946f677f8ff0e3c0a1e4438b5fcf1c4798ff13e6a","abstract_canon_sha256":"36919784a0760df134c8e26356f5b7277c6c6806db3a54da9ae0e96a656f7885"},"schema_version":"1.0"},"canonical_sha256":"a5ac7489276c64f3725d1865fd21af7cbc15fb9d2268714d1fac6cfe0d0200ce","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:34:04.141472Z","signature_b64":"IdBgHcM6QEWwKaYOKLxTBFfXdxzrA8aZqK6Tmx0A8eHT9T7KdqxQ9rPaj0UIzeokvoYvF3VW+kAFLq5k/lvFBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5ac7489276c64f3725d1865fd21af7cbc15fb9d2268714d1fac6cfe0d0200ce","last_reissued_at":"2026-07-05T08:34:04.140936Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:34:04.140936Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.01646","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:34:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w+J9AFCrw5wX9B8wdoIbrbzKnmVzwOGsHQS/YCEuiQKGFEOciUzOIsTNdjVy4Fr5+uqKRKZ6FLwTd+0ZSpOSDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T09:18:53.190225Z"},"content_sha256":"a861fb40097a73b1deed87aac30e84707b6d7e01d47f24757dc38837da003bc6","schema_version":"1.0","event_id":"sha256:a861fb40097a73b1deed87aac30e84707b6d7e01d47f24757dc38837da003bc6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:UWWHJCJHNRSPG4S5DBS72INPPS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Lele Wang, Qi Yan, Renjie Liao, Yang Song, Zhengyang Liang","submitted_at":"2023-07-04T10:58:42Z","abstract_excerpt":"Diffusion models based on permutation-equivariant networks can learn permutation-invariant distributions for graph data. However, in comparison to their non-invariant counterparts, we have found that these invariant models encounter greater learning challenges since 1) their effective target distributions exhibit more modes; 2) their optimal one-step denoising scores are the score functions of Gaussian mixtures with more components. Motivated by this analysis, we propose a non-invariant diffusion model, called $\\textit{SwinGNN}$, which employs an efficient edge-to-edge 2-WL message passing net"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.01646","kind":"arxiv","version":4},"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/2307.01646/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":"2026-07-05T08:34:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mv14JQljl57ejBDpg2IoT3Qp7+S18xFubauqcDmFuUxUHf9RZKc4TscnPmWuq2swJdIn7wAC1uN7k+5CpbJ3BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T09:18:53.190726Z"},"content_sha256":"9e14dd530509694370d0ecf325e45dade0ec72e1d535d129c4bdfd9c7a2dd9da","schema_version":"1.0","event_id":"sha256:9e14dd530509694370d0ecf325e45dade0ec72e1d535d129c4bdfd9c7a2dd9da"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UWWHJCJHNRSPG4S5DBS72INPPS/bundle.json","state_url":"https://pith.science/pith/UWWHJCJHNRSPG4S5DBS72INPPS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UWWHJCJHNRSPG4S5DBS72INPPS/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":"2026-08-08T09:18:53Z","links":{"resolver":"https://pith.science/pith/UWWHJCJHNRSPG4S5DBS72INPPS","bundle":"https://pith.science/pith/UWWHJCJHNRSPG4S5DBS72INPPS/bundle.json","state":"https://pith.science/pith/UWWHJCJHNRSPG4S5DBS72INPPS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UWWHJCJHNRSPG4S5DBS72INPPS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:UWWHJCJHNRSPG4S5DBS72INPPS","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":"36919784a0760df134c8e26356f5b7277c6c6806db3a54da9ae0e96a656f7885","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-04T10:58:42Z","title_canon_sha256":"bd781d0540441d3641c45fa946f677f8ff0e3c0a1e4438b5fcf1c4798ff13e6a"},"schema_version":"1.0","source":{"id":"2307.01646","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.01646","created_at":"2026-07-05T08:34:04Z"},{"alias_kind":"arxiv_version","alias_value":"2307.01646v4","created_at":"2026-07-05T08:34:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.01646","created_at":"2026-07-05T08:34:04Z"},{"alias_kind":"pith_short_12","alias_value":"UWWHJCJHNRSP","created_at":"2026-07-05T08:34:04Z"},{"alias_kind":"pith_short_16","alias_value":"UWWHJCJHNRSPG4S5","created_at":"2026-07-05T08:34:04Z"},{"alias_kind":"pith_short_8","alias_value":"UWWHJCJH","created_at":"2026-07-05T08:34:04Z"}],"graph_snapshots":[{"event_id":"sha256:9e14dd530509694370d0ecf325e45dade0ec72e1d535d129c4bdfd9c7a2dd9da","target":"graph","created_at":"2026-07-05T08:34:04Z","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/2307.01646/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion models based on permutation-equivariant networks can learn permutation-invariant distributions for graph data. However, in comparison to their non-invariant counterparts, we have found that these invariant models encounter greater learning challenges since 1) their effective target distributions exhibit more modes; 2) their optimal one-step denoising scores are the score functions of Gaussian mixtures with more components. Motivated by this analysis, we propose a non-invariant diffusion model, called $\\textit{SwinGNN}$, which employs an efficient edge-to-edge 2-WL message passing net","authors_text":"Lele Wang, Qi Yan, Renjie Liao, Yang Song, Zhengyang Liang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-04T10:58:42Z","title":"SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.01646","kind":"arxiv","version":4},"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:a861fb40097a73b1deed87aac30e84707b6d7e01d47f24757dc38837da003bc6","target":"record","created_at":"2026-07-05T08:34:04Z","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":"36919784a0760df134c8e26356f5b7277c6c6806db3a54da9ae0e96a656f7885","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-04T10:58:42Z","title_canon_sha256":"bd781d0540441d3641c45fa946f677f8ff0e3c0a1e4438b5fcf1c4798ff13e6a"},"schema_version":"1.0","source":{"id":"2307.01646","kind":"arxiv","version":4}},"canonical_sha256":"a5ac7489276c64f3725d1865fd21af7cbc15fb9d2268714d1fac6cfe0d0200ce","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a5ac7489276c64f3725d1865fd21af7cbc15fb9d2268714d1fac6cfe0d0200ce","first_computed_at":"2026-07-05T08:34:04.140936Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:34:04.140936Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IdBgHcM6QEWwKaYOKLxTBFfXdxzrA8aZqK6Tmx0A8eHT9T7KdqxQ9rPaj0UIzeokvoYvF3VW+kAFLq5k/lvFBA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:34:04.141472Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.01646","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a861fb40097a73b1deed87aac30e84707b6d7e01d47f24757dc38837da003bc6","sha256:9e14dd530509694370d0ecf325e45dade0ec72e1d535d129c4bdfd9c7a2dd9da"],"state_sha256":"11f7da563d8f6f2fe89246c5c72332571a65bb9305a3ca7e176852d638ba9757"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+B4RYlcueBXk/pGqwYVrq2XIlkCmFJ0ZGda9xC22BizPR31LowZRerJ3IZxBzZsUIrBaLAX5NsqSMHOI21vGBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T09:18:53.199513Z","bundle_sha256":"34a469810a81a3ce3247c265cca35acf99780a7487bb286005c6aeb7cf51b4b2"}}