{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2010:GEJ2YB3U57HHJMXS74YR5JFEIT","short_pith_number":"pith:GEJ2YB3U","schema_version":"1.0","canonical_sha256":"3113ac0774efce74b2f2ff311ea4a444f80abf6e143d01dfacdc4852f69296c7","source":{"kind":"arxiv","id":"1011.1937","version":2},"attestation_state":"computed","paper":{"title":"A Separable Model for Dynamic Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Los Angeles), Mark S. Handcock (Department of Statistics, Pavel N. Krivitsky (Department of Statistics, Penn State University, University of California, University Park)","submitted_at":"2010-11-08T22:47:13Z","abstract_excerpt":"Models of dynamic networks --- networks that evolve over time --- have manifold applications. We develop a discrete-time generative model for social network evolution that inherits the richness and flexibility of the class of exponential-family random graph models. The model --- a Separable Temporal ERGM (STERGM) --- facilitates separable modeling of the tie duration distributions and the structural dynamics of tie formation. We develop likelihood-based inference for the model, and provide computational algorithms for maximum likelihood estimation. We illustrate the interpretability of the mod"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1011.1937","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2010-11-08T22:47:13Z","cross_cats_sorted":[],"title_canon_sha256":"37643c65b159c90133f17e34970f20ce656a11d620c153ba6d29061196fefb98","abstract_canon_sha256":"d25207b5d280be7688577c9533d1104a798399a62cc8a62550eedccd5c3ff1b6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:20:48.166157Z","signature_b64":"yZnBC5LUGUiwtS9gvSxP3FGxh3SWAPxoh7qCgXwl4mrVCrn+pJVbIwAov40umeKXJfUp2TP9veDf0RgvTOvGCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3113ac0774efce74b2f2ff311ea4a444f80abf6e143d01dfacdc4852f69296c7","last_reissued_at":"2026-05-18T02:20:48.165423Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:20:48.165423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Separable Model for Dynamic Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Los Angeles), Mark S. Handcock (Department of Statistics, Pavel N. Krivitsky (Department of Statistics, Penn State University, University of California, University Park)","submitted_at":"2010-11-08T22:47:13Z","abstract_excerpt":"Models of dynamic networks --- networks that evolve over time --- have manifold applications. We develop a discrete-time generative model for social network evolution that inherits the richness and flexibility of the class of exponential-family random graph models. The model --- a Separable Temporal ERGM (STERGM) --- facilitates separable modeling of the tie duration distributions and the structural dynamics of tie formation. We develop likelihood-based inference for the model, and provide computational algorithms for maximum likelihood estimation. We illustrate the interpretability of the mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1011.1937","kind":"arxiv","version":2},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1011.1937","created_at":"2026-05-18T02:20:48.165586+00:00"},{"alias_kind":"arxiv_version","alias_value":"1011.1937v2","created_at":"2026-05-18T02:20:48.165586+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1011.1937","created_at":"2026-05-18T02:20:48.165586+00:00"},{"alias_kind":"pith_short_12","alias_value":"GEJ2YB3U57HH","created_at":"2026-05-18T12:26:07.630475+00:00"},{"alias_kind":"pith_short_16","alias_value":"GEJ2YB3U57HHJMXS","created_at":"2026-05-18T12:26:07.630475+00:00"},{"alias_kind":"pith_short_8","alias_value":"GEJ2YB3U","created_at":"2026-05-18T12:26:07.630475+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GEJ2YB3U57HHJMXS74YR5JFEIT","json":"https://pith.science/pith/GEJ2YB3U57HHJMXS74YR5JFEIT.json","graph_json":"https://pith.science/api/pith-number/GEJ2YB3U57HHJMXS74YR5JFEIT/graph.json","events_json":"https://pith.science/api/pith-number/GEJ2YB3U57HHJMXS74YR5JFEIT/events.json","paper":"https://pith.science/paper/GEJ2YB3U"},"agent_actions":{"view_html":"https://pith.science/pith/GEJ2YB3U57HHJMXS74YR5JFEIT","download_json":"https://pith.science/pith/GEJ2YB3U57HHJMXS74YR5JFEIT.json","view_paper":"https://pith.science/paper/GEJ2YB3U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1011.1937&json=true","fetch_graph":"https://pith.science/api/pith-number/GEJ2YB3U57HHJMXS74YR5JFEIT/graph.json","fetch_events":"https://pith.science/api/pith-number/GEJ2YB3U57HHJMXS74YR5JFEIT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GEJ2YB3U57HHJMXS74YR5JFEIT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GEJ2YB3U57HHJMXS74YR5JFEIT/action/storage_attestation","attest_author":"https://pith.science/pith/GEJ2YB3U57HHJMXS74YR5JFEIT/action/author_attestation","sign_citation":"https://pith.science/pith/GEJ2YB3U57HHJMXS74YR5JFEIT/action/citation_signature","submit_replication":"https://pith.science/pith/GEJ2YB3U57HHJMXS74YR5JFEIT/action/replication_record"}},"created_at":"2026-05-18T02:20:48.165586+00:00","updated_at":"2026-05-18T02:20:48.165586+00:00"}