{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:FP5KFU73MYH7ZETXUS5OVVM3A3","short_pith_number":"pith:FP5KFU73","schema_version":"1.0","canonical_sha256":"2bfaa2d3fb660ffc9277a4baead59b06e68bccafc069bb9cc48f30b297f80ad4","source":{"kind":"arxiv","id":"1510.01157","version":2},"attestation_state":"computed","paper":{"title":"A Note on Jointly Modeling Edges and Node Attributes of a Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.PR","authors_text":"Haiyan Cai","submitted_at":"2015-10-05T13:58:15Z","abstract_excerpt":"We are interested in modeling networks in which the connectivity among the nodes and node attributes are random variables and interact with each other. We propose a probabilistic model that allows one to formulate jointly a probability distribution for these variables. This model can be described as a combination of a latent space model and a Gaussian graphical model: given the node variables, the edges will follow independent logistic distributions, with the node variables as covariates; given edges, the node variables will be distributed jointly as multivariate Gaussian, with their condition"},"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":"1510.01157","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.PR","submitted_at":"2015-10-05T13:58:15Z","cross_cats_sorted":[],"title_canon_sha256":"a48c828ccaacc1bfb2e2155378134a34cb44d3d82d7ae08b217d8f6c75d31bdd","abstract_canon_sha256":"df5c7aa739a70a94502f1e9ae47490987a83a819193db77087a7f27b4e05c80f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:05:45.893994Z","signature_b64":"Q8L5C2amSt0hsYJXEpMobaUfcxg3fjbXYwRMbpIBkvz3xnX6IdAs02ASQiFNRZU055iOowtuKcKA5HqwXoCGDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2bfaa2d3fb660ffc9277a4baead59b06e68bccafc069bb9cc48f30b297f80ad4","last_reissued_at":"2026-05-18T01:05:45.893511Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:05:45.893511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Note on Jointly Modeling Edges and Node Attributes of a Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.PR","authors_text":"Haiyan Cai","submitted_at":"2015-10-05T13:58:15Z","abstract_excerpt":"We are interested in modeling networks in which the connectivity among the nodes and node attributes are random variables and interact with each other. We propose a probabilistic model that allows one to formulate jointly a probability distribution for these variables. This model can be described as a combination of a latent space model and a Gaussian graphical model: given the node variables, the edges will follow independent logistic distributions, with the node variables as covariates; given edges, the node variables will be distributed jointly as multivariate Gaussian, with their condition"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.01157","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":"1510.01157","created_at":"2026-05-18T01:05:45.893583+00:00"},{"alias_kind":"arxiv_version","alias_value":"1510.01157v2","created_at":"2026-05-18T01:05:45.893583+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.01157","created_at":"2026-05-18T01:05:45.893583+00:00"},{"alias_kind":"pith_short_12","alias_value":"FP5KFU73MYH7","created_at":"2026-05-18T12:29:19.899920+00:00"},{"alias_kind":"pith_short_16","alias_value":"FP5KFU73MYH7ZETX","created_at":"2026-05-18T12:29:19.899920+00:00"},{"alias_kind":"pith_short_8","alias_value":"FP5KFU73","created_at":"2026-05-18T12:29:19.899920+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/FP5KFU73MYH7ZETXUS5OVVM3A3","json":"https://pith.science/pith/FP5KFU73MYH7ZETXUS5OVVM3A3.json","graph_json":"https://pith.science/api/pith-number/FP5KFU73MYH7ZETXUS5OVVM3A3/graph.json","events_json":"https://pith.science/api/pith-number/FP5KFU73MYH7ZETXUS5OVVM3A3/events.json","paper":"https://pith.science/paper/FP5KFU73"},"agent_actions":{"view_html":"https://pith.science/pith/FP5KFU73MYH7ZETXUS5OVVM3A3","download_json":"https://pith.science/pith/FP5KFU73MYH7ZETXUS5OVVM3A3.json","view_paper":"https://pith.science/paper/FP5KFU73","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1510.01157&json=true","fetch_graph":"https://pith.science/api/pith-number/FP5KFU73MYH7ZETXUS5OVVM3A3/graph.json","fetch_events":"https://pith.science/api/pith-number/FP5KFU73MYH7ZETXUS5OVVM3A3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FP5KFU73MYH7ZETXUS5OVVM3A3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FP5KFU73MYH7ZETXUS5OVVM3A3/action/storage_attestation","attest_author":"https://pith.science/pith/FP5KFU73MYH7ZETXUS5OVVM3A3/action/author_attestation","sign_citation":"https://pith.science/pith/FP5KFU73MYH7ZETXUS5OVVM3A3/action/citation_signature","submit_replication":"https://pith.science/pith/FP5KFU73MYH7ZETXUS5OVVM3A3/action/replication_record"}},"created_at":"2026-05-18T01:05:45.893583+00:00","updated_at":"2026-05-18T01:05:45.893583+00:00"}