{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:HIYUUL6WOXFSQSRTU5U4TSOMCE","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":"f597ccba06f69e9fb54345cc32627d5fbfcbd47f16de591de2a1ed875630c48d","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-28T16:16:14Z","title_canon_sha256":"ab0bcbacd5cf769a63784b98c7d7b7e8df562860ea4001d491e084b39b7f568b"},"schema_version":"1.0","source":{"id":"1803.10705","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.10705","created_at":"2026-05-18T00:19:54Z"},{"alias_kind":"arxiv_version","alias_value":"1803.10705v1","created_at":"2026-05-18T00:19:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.10705","created_at":"2026-05-18T00:19:54Z"},{"alias_kind":"pith_short_12","alias_value":"HIYUUL6WOXFS","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HIYUUL6WOXFSQSRT","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HIYUUL6W","created_at":"2026-05-18T12:32:28Z"}],"graph_snapshots":[{"event_id":"sha256:5336679d138dc8907f83256b06930e19c3d9e1726980861c3ebf968a85e8f140","target":"graph","created_at":"2026-05-18T00:19:54Z","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":"Conditional probabilistic graphical models provide a powerful framework for structured regression in spatio-temporal datasets with complex correlation patterns. However, in real-life applications a large fraction of observations is often missing, which can severely limit the representational power of these models. In this paper we propose a Marginalized Gaussian Conditional Random Fields (m-GCRF) structured regression model for dealing with missing labels in partially observed temporal attributed graphs. This method is aimed at learning with both labeled and unlabeled parts and effectively pre","authors_text":"Djordje Gligorijevic, Jelena Stojanovic, Milos Jovanovic, Zoran Obradovic","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-28T16:16:14Z","title":"Semi-supervised learning for structured regression on partially observed attributed graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.10705","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":"sha256:085a2c8616c110977ad6d683e5a23d6a435fc2d24891265fddaa1f5193d84b81","target":"record","created_at":"2026-05-18T00:19:54Z","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":"f597ccba06f69e9fb54345cc32627d5fbfcbd47f16de591de2a1ed875630c48d","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-28T16:16:14Z","title_canon_sha256":"ab0bcbacd5cf769a63784b98c7d7b7e8df562860ea4001d491e084b39b7f568b"},"schema_version":"1.0","source":{"id":"1803.10705","kind":"arxiv","version":1}},"canonical_sha256":"3a314a2fd675cb284a33a769c9c9cc1118a9867ffa53e2e56cd25e10ea2c17c3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3a314a2fd675cb284a33a769c9c9cc1118a9867ffa53e2e56cd25e10ea2c17c3","first_computed_at":"2026-05-18T00:19:54.411812Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:19:54.411812Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5cLlYLav538jBy8af4zJsbnI0ZGkE/aVj14wHtAyyTwY5lv19WjdE+lfoQgx40hAEfleQPp3ymFm9qwMO/VhCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:19:54.412580Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.10705","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:085a2c8616c110977ad6d683e5a23d6a435fc2d24891265fddaa1f5193d84b81","sha256:5336679d138dc8907f83256b06930e19c3d9e1726980861c3ebf968a85e8f140"],"state_sha256":"e16659ddc32b0a10eba00ada11004fb23c9d312b43af6cec477e050415e4718d"}