{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:4OPGN2BV2CGXL3EHAHKNZ52K4A","short_pith_number":"pith:4OPGN2BV","canonical_record":{"source":{"id":"1805.09567","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-24T09:27:57Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5821d5eab2044d4235fbf24e162a3de0f8b3d49e75c6c9568da10094d47db2d4","abstract_canon_sha256":"5ea7eb303483896f9a0c2ec7d841d9cd23746031faab33e9d464ab08cad8bc17"},"schema_version":"1.0"},"canonical_sha256":"e39e66e835d08d75ec8701d4dcf74ae038409dcb3932734df27301ea48951754","source":{"kind":"arxiv","id":"1805.09567","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.09567","created_at":"2026-05-18T00:15:03Z"},{"alias_kind":"arxiv_version","alias_value":"1805.09567v1","created_at":"2026-05-18T00:15:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.09567","created_at":"2026-05-18T00:15:03Z"},{"alias_kind":"pith_short_12","alias_value":"4OPGN2BV2CGX","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"4OPGN2BV2CGXL3EH","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"4OPGN2BV","created_at":"2026-05-18T12:32:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:4OPGN2BV2CGXL3EHAHKNZ52K4A","target":"record","payload":{"canonical_record":{"source":{"id":"1805.09567","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-24T09:27:57Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5821d5eab2044d4235fbf24e162a3de0f8b3d49e75c6c9568da10094d47db2d4","abstract_canon_sha256":"5ea7eb303483896f9a0c2ec7d841d9cd23746031faab33e9d464ab08cad8bc17"},"schema_version":"1.0"},"canonical_sha256":"e39e66e835d08d75ec8701d4dcf74ae038409dcb3932734df27301ea48951754","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:03.554868Z","signature_b64":"Z1CJHg5TcZ5yGMGZw9HIrn73CT27ClqM6JzUblmKnDUrwDeZnw7x6xI/Lu7hd0yWZ+jG9ORN/b60XDtI7tAuBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e39e66e835d08d75ec8701d4dcf74ae038409dcb3932734df27301ea48951754","last_reissued_at":"2026-05-18T00:15:03.554208Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:03.554208Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.09567","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":"2026-05-18T00:15:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A3tOl+Efl1vdGyyQB1ZqtrlQULmUXzcUqy9+vkfCGxz2cwJ0BC75SemeLGY3AoG52484SdP8UUCpg/Iab+49Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T05:23:42.112040Z"},"content_sha256":"ba85aff5600cbbe316c2719d8720c303f08558908140429eb1c3a1bf12cbeb83","schema_version":"1.0","event_id":"sha256:ba85aff5600cbbe316c2719d8720c303f08558908140429eb1c3a1bf12cbeb83"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:4OPGN2BV2CGXL3EHAHKNZ52K4A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Aapo Hyv\\\"arinen, Ricardo Pio Monti","submitted_at":"2018-05-24T09:27:57Z","abstract_excerpt":"Connectivity estimation is challenging in the context of high-dimensional data. A useful preprocessing step is to group variables into clusters, however, it is not always clear how to do so from the perspective of connectivity estimation. Another practical challenge is that we may have data from multiple related classes (e.g., multiple subjects or conditions) and wish to incorporate constraints on the similarities across classes. We propose a probabilistic model which simultaneously performs both a grouping of variables (i.e., detecting community structure) and estimation of connectivities bet"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.09567","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":""},"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":"2026-05-18T00:15:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J7x60dPMOQ9kvxi7VP7rDZxZpGNYKF0VEKGOhLiK19a3E1Tros3J/33kXSBTwrzsqsyvhOrip5mnaTJr4FtwCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T05:23:42.112397Z"},"content_sha256":"920d9244f23091a03eac7e96451e5f8768b4d09180591db9f655e062107c1603","schema_version":"1.0","event_id":"sha256:920d9244f23091a03eac7e96451e5f8768b4d09180591db9f655e062107c1603"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4OPGN2BV2CGXL3EHAHKNZ52K4A/bundle.json","state_url":"https://pith.science/pith/4OPGN2BV2CGXL3EHAHKNZ52K4A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4OPGN2BV2CGXL3EHAHKNZ52K4A/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":"2026-06-03T05:23:42Z","links":{"resolver":"https://pith.science/pith/4OPGN2BV2CGXL3EHAHKNZ52K4A","bundle":"https://pith.science/pith/4OPGN2BV2CGXL3EHAHKNZ52K4A/bundle.json","state":"https://pith.science/pith/4OPGN2BV2CGXL3EHAHKNZ52K4A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4OPGN2BV2CGXL3EHAHKNZ52K4A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:4OPGN2BV2CGXL3EHAHKNZ52K4A","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":"5ea7eb303483896f9a0c2ec7d841d9cd23746031faab33e9d464ab08cad8bc17","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-24T09:27:57Z","title_canon_sha256":"5821d5eab2044d4235fbf24e162a3de0f8b3d49e75c6c9568da10094d47db2d4"},"schema_version":"1.0","source":{"id":"1805.09567","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.09567","created_at":"2026-05-18T00:15:03Z"},{"alias_kind":"arxiv_version","alias_value":"1805.09567v1","created_at":"2026-05-18T00:15:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.09567","created_at":"2026-05-18T00:15:03Z"},{"alias_kind":"pith_short_12","alias_value":"4OPGN2BV2CGX","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"4OPGN2BV2CGXL3EH","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"4OPGN2BV","created_at":"2026-05-18T12:32:05Z"}],"graph_snapshots":[{"event_id":"sha256:920d9244f23091a03eac7e96451e5f8768b4d09180591db9f655e062107c1603","target":"graph","created_at":"2026-05-18T00:15:03Z","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"},"paper":{"abstract_excerpt":"Connectivity estimation is challenging in the context of high-dimensional data. A useful preprocessing step is to group variables into clusters, however, it is not always clear how to do so from the perspective of connectivity estimation. Another practical challenge is that we may have data from multiple related classes (e.g., multiple subjects or conditions) and wish to incorporate constraints on the similarities across classes. We propose a probabilistic model which simultaneously performs both a grouping of variables (i.e., detecting community structure) and estimation of connectivities bet","authors_text":"Aapo Hyv\\\"arinen, Ricardo Pio Monti","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-24T09:27:57Z","title":"A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.09567","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:ba85aff5600cbbe316c2719d8720c303f08558908140429eb1c3a1bf12cbeb83","target":"record","created_at":"2026-05-18T00:15:03Z","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":"5ea7eb303483896f9a0c2ec7d841d9cd23746031faab33e9d464ab08cad8bc17","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-24T09:27:57Z","title_canon_sha256":"5821d5eab2044d4235fbf24e162a3de0f8b3d49e75c6c9568da10094d47db2d4"},"schema_version":"1.0","source":{"id":"1805.09567","kind":"arxiv","version":1}},"canonical_sha256":"e39e66e835d08d75ec8701d4dcf74ae038409dcb3932734df27301ea48951754","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e39e66e835d08d75ec8701d4dcf74ae038409dcb3932734df27301ea48951754","first_computed_at":"2026-05-18T00:15:03.554208Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:03.554208Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z1CJHg5TcZ5yGMGZw9HIrn73CT27ClqM6JzUblmKnDUrwDeZnw7x6xI/Lu7hd0yWZ+jG9ORN/b60XDtI7tAuBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:03.554868Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.09567","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ba85aff5600cbbe316c2719d8720c303f08558908140429eb1c3a1bf12cbeb83","sha256:920d9244f23091a03eac7e96451e5f8768b4d09180591db9f655e062107c1603"],"state_sha256":"46bc9d5ad125a5384216613c6e7fb00680162ca4793b1eaef92a5609e6b54f3f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MG2VDlS9+ICGe7ELrQ40TMnsAbzNpHNf+mxezZ9GV2QNnjZ++8phI5MAOegE3+Mb6Z8PnTyJer2sNI9LDdQsAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T05:23:42.114364Z","bundle_sha256":"309f15905910beda19b09600bc9957dadffbcf504b4d19a820d061b237b760aa"}}