{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:VGJTSZER5OJJ52ZTUBIHLRTRQP","short_pith_number":"pith:VGJTSZER","canonical_record":{"source":{"id":"2311.10638","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-17T16:50:00Z","cross_cats_sorted":["cs.AI","stat.ME"],"title_canon_sha256":"c16b52b9200c053372e8d476e640a2bcfb23f1193a1227be25664827a21eb040","abstract_canon_sha256":"281c48d63f817287333cd159af13ab4a91c8db0e63d8927c79489be64aba1bbd"},"schema_version":"1.0"},"canonical_sha256":"a993396491eb929eeb33a05075c67183fe7a08f445ffc5b80c9942abed75c5ae","source":{"kind":"arxiv","id":"2311.10638","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.10638","created_at":"2026-07-05T07:13:55Z"},{"alias_kind":"arxiv_version","alias_value":"2311.10638v1","created_at":"2026-07-05T07:13:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.10638","created_at":"2026-07-05T07:13:55Z"},{"alias_kind":"pith_short_12","alias_value":"VGJTSZER5OJJ","created_at":"2026-07-05T07:13:55Z"},{"alias_kind":"pith_short_16","alias_value":"VGJTSZER5OJJ52ZT","created_at":"2026-07-05T07:13:55Z"},{"alias_kind":"pith_short_8","alias_value":"VGJTSZER","created_at":"2026-07-05T07:13:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:VGJTSZER5OJJ52ZTUBIHLRTRQP","target":"record","payload":{"canonical_record":{"source":{"id":"2311.10638","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-17T16:50:00Z","cross_cats_sorted":["cs.AI","stat.ME"],"title_canon_sha256":"c16b52b9200c053372e8d476e640a2bcfb23f1193a1227be25664827a21eb040","abstract_canon_sha256":"281c48d63f817287333cd159af13ab4a91c8db0e63d8927c79489be64aba1bbd"},"schema_version":"1.0"},"canonical_sha256":"a993396491eb929eeb33a05075c67183fe7a08f445ffc5b80c9942abed75c5ae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:13:55.077613Z","signature_b64":"FcmQGyVJrg1lMYH+USlzwqzFsoDv4ubTfD8smgeUWSemgi/CrYX2F4So/nceraIe15HLUmZIpgD799w9YJgpCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a993396491eb929eeb33a05075c67183fe7a08f445ffc5b80c9942abed75c5ae","last_reissued_at":"2026-07-05T07:13:55.077142Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:13:55.077142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.10638","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":"2026-07-05T07:13:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+4Fp7C930K3auwyOqWwJ/pUjeoOpc9Meu+IEOQ2h23YWR2x95sWTJyhJ48EeCfK1mzj8B9upRY3Hp2cU3j1/CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T18:34:29.365277Z"},"content_sha256":"25bd604ffdc61a16b70678d444cf3c7f5a818af0c82d9a3fd048a243196491a4","schema_version":"1.0","event_id":"sha256:25bd604ffdc61a16b70678d444cf3c7f5a818af0c82d9a3fd048a243196491a4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:VGJTSZER5OJJ52ZTUBIHLRTRQP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Concept-free Causal Disentanglement with Variational Graph Auto-Encoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ME"],"primary_cat":"cs.LG","authors_text":"Jingyun Feng, Lili Yang, Lin Zhang","submitted_at":"2023-11-17T16:50:00Z","abstract_excerpt":"In disentangled representation learning, the goal is to achieve a compact representation that consists of all interpretable generative factors in the observational data. Learning disentangled representations for graphs becomes increasingly important as graph data rapidly grows. Existing approaches often rely on Variational Auto-Encoder (VAE) or its causal structure learning-based refinement, which suffer from sub-optimality in VAEs due to the independence factor assumption and unavailability of concept labels, respectively. In this paper, we propose an unsupervised solution, dubbed concept-fre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.10638","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/2311.10638/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":"2026-07-05T07:13:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gPzi2GoRj7IEgG6uEvDBYqeBs2y3grE+qFPXskNtPzdHhif88EEdZsRKzyG7vnVPACEtFZ3e1K/0l4s0uv8lAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T18:34:29.365773Z"},"content_sha256":"3f20e39da152c8dfde90a81b9d7d2c8389773215e30cb4f9d24de49620c3faa0","schema_version":"1.0","event_id":"sha256:3f20e39da152c8dfde90a81b9d7d2c8389773215e30cb4f9d24de49620c3faa0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VGJTSZER5OJJ52ZTUBIHLRTRQP/bundle.json","state_url":"https://pith.science/pith/VGJTSZER5OJJ52ZTUBIHLRTRQP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VGJTSZER5OJJ52ZTUBIHLRTRQP/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":"2026-08-09T18:34:29Z","links":{"resolver":"https://pith.science/pith/VGJTSZER5OJJ52ZTUBIHLRTRQP","bundle":"https://pith.science/pith/VGJTSZER5OJJ52ZTUBIHLRTRQP/bundle.json","state":"https://pith.science/pith/VGJTSZER5OJJ52ZTUBIHLRTRQP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VGJTSZER5OJJ52ZTUBIHLRTRQP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:VGJTSZER5OJJ52ZTUBIHLRTRQP","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":"281c48d63f817287333cd159af13ab4a91c8db0e63d8927c79489be64aba1bbd","cross_cats_sorted":["cs.AI","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-17T16:50:00Z","title_canon_sha256":"c16b52b9200c053372e8d476e640a2bcfb23f1193a1227be25664827a21eb040"},"schema_version":"1.0","source":{"id":"2311.10638","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.10638","created_at":"2026-07-05T07:13:55Z"},{"alias_kind":"arxiv_version","alias_value":"2311.10638v1","created_at":"2026-07-05T07:13:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.10638","created_at":"2026-07-05T07:13:55Z"},{"alias_kind":"pith_short_12","alias_value":"VGJTSZER5OJJ","created_at":"2026-07-05T07:13:55Z"},{"alias_kind":"pith_short_16","alias_value":"VGJTSZER5OJJ52ZT","created_at":"2026-07-05T07:13:55Z"},{"alias_kind":"pith_short_8","alias_value":"VGJTSZER","created_at":"2026-07-05T07:13:55Z"}],"graph_snapshots":[{"event_id":"sha256:3f20e39da152c8dfde90a81b9d7d2c8389773215e30cb4f9d24de49620c3faa0","target":"graph","created_at":"2026-07-05T07:13:55Z","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/2311.10638/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In disentangled representation learning, the goal is to achieve a compact representation that consists of all interpretable generative factors in the observational data. Learning disentangled representations for graphs becomes increasingly important as graph data rapidly grows. Existing approaches often rely on Variational Auto-Encoder (VAE) or its causal structure learning-based refinement, which suffer from sub-optimality in VAEs due to the independence factor assumption and unavailability of concept labels, respectively. In this paper, we propose an unsupervised solution, dubbed concept-fre","authors_text":"Jingyun Feng, Lili Yang, Lin Zhang","cross_cats":["cs.AI","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-17T16:50:00Z","title":"Concept-free Causal Disentanglement with Variational Graph Auto-Encoder"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.10638","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":"sha256:25bd604ffdc61a16b70678d444cf3c7f5a818af0c82d9a3fd048a243196491a4","target":"record","created_at":"2026-07-05T07:13:55Z","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":"281c48d63f817287333cd159af13ab4a91c8db0e63d8927c79489be64aba1bbd","cross_cats_sorted":["cs.AI","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-17T16:50:00Z","title_canon_sha256":"c16b52b9200c053372e8d476e640a2bcfb23f1193a1227be25664827a21eb040"},"schema_version":"1.0","source":{"id":"2311.10638","kind":"arxiv","version":1}},"canonical_sha256":"a993396491eb929eeb33a05075c67183fe7a08f445ffc5b80c9942abed75c5ae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a993396491eb929eeb33a05075c67183fe7a08f445ffc5b80c9942abed75c5ae","first_computed_at":"2026-07-05T07:13:55.077142Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:13:55.077142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FcmQGyVJrg1lMYH+USlzwqzFsoDv4ubTfD8smgeUWSemgi/CrYX2F4So/nceraIe15HLUmZIpgD799w9YJgpCA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:13:55.077613Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.10638","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:25bd604ffdc61a16b70678d444cf3c7f5a818af0c82d9a3fd048a243196491a4","sha256:3f20e39da152c8dfde90a81b9d7d2c8389773215e30cb4f9d24de49620c3faa0"],"state_sha256":"81c8733f792adb03815cac2e136e141d63d97fa0a863d13eb148fa6bcb8c2094"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wj55G4VPAsmogM78PxGWuyOqv0i0WnLXW5Fh17IzixDISc7dEl7Mf+6v3idK1aEFZYPPW6qlFnMi0gtQ1ODZDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T18:34:29.369535Z","bundle_sha256":"a804d3a8ffe1576ba1880696b50490bf1042b10ff50f8f9db08024078e7fedc0"}}