{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:6GZWCVFFASZ6GIOXFBWMDDBVED","short_pith_number":"pith:6GZWCVFF","schema_version":"1.0","canonical_sha256":"f1b36154a504b3e321d7286cc18c3520ff02c3071440e320008228f12218c81f","source":{"kind":"arxiv","id":"1804.05816","version":1},"attestation_state":"computed","paper":{"title":"Models for Capturing Temporal Smoothness in Evolving Networks for Learning Latent Representation of Nodes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.SI","authors_text":"Mohammad Al Hasan, Nicholas K. Varberg, Shafiq Joty, Tanay Kumar Saha, Thomas Williams","submitted_at":"2018-04-16T17:40:02Z","abstract_excerpt":"In a dynamic network, the neighborhood of the vertices evolve across different temporal snapshots of the network. Accurate modeling of this temporal evolution can help solve complex tasks involving real-life social and interaction networks. However, existing models for learning latent representation are inadequate for obtaining the representation vectors of the vertices for different time-stamps of a dynamic network in a meaningful way. In this paper, we propose latent representation learning models for dynamic networks which overcome the above limitation by considering two different kinds of "},"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":"1804.05816","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-04-16T17:40:02Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"09b2323b4b4a22d23198d657f8696032976302403a1994394d1722f3ee326601","abstract_canon_sha256":"50c40b031fc68ccbd5af43ef98d433c4ca9e41774131eb174b7f4c9b0de2f7a0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:27.338750Z","signature_b64":"DYHM3FIr3NOaW1As0Beg3gycl525hGadqncaa0WbfPauKU6qlcsmlA47BJalnnZwddDu3Hn+llJtMIWD7bc1AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f1b36154a504b3e321d7286cc18c3520ff02c3071440e320008228f12218c81f","last_reissued_at":"2026-05-18T00:18:27.338369Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:27.338369Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Models for Capturing Temporal Smoothness in Evolving Networks for Learning Latent Representation of Nodes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.SI","authors_text":"Mohammad Al Hasan, Nicholas K. Varberg, Shafiq Joty, Tanay Kumar Saha, Thomas Williams","submitted_at":"2018-04-16T17:40:02Z","abstract_excerpt":"In a dynamic network, the neighborhood of the vertices evolve across different temporal snapshots of the network. Accurate modeling of this temporal evolution can help solve complex tasks involving real-life social and interaction networks. However, existing models for learning latent representation are inadequate for obtaining the representation vectors of the vertices for different time-stamps of a dynamic network in a meaningful way. In this paper, we propose latent representation learning models for dynamic networks which overcome the above limitation by considering two different kinds of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.05816","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1804.05816","created_at":"2026-05-18T00:18:27.338420+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.05816v1","created_at":"2026-05-18T00:18:27.338420+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.05816","created_at":"2026-05-18T00:18:27.338420+00:00"},{"alias_kind":"pith_short_12","alias_value":"6GZWCVFFASZ6","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"6GZWCVFFASZ6GIOX","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"6GZWCVFF","created_at":"2026-05-18T12:32:08.215937+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/6GZWCVFFASZ6GIOXFBWMDDBVED","json":"https://pith.science/pith/6GZWCVFFASZ6GIOXFBWMDDBVED.json","graph_json":"https://pith.science/api/pith-number/6GZWCVFFASZ6GIOXFBWMDDBVED/graph.json","events_json":"https://pith.science/api/pith-number/6GZWCVFFASZ6GIOXFBWMDDBVED/events.json","paper":"https://pith.science/paper/6GZWCVFF"},"agent_actions":{"view_html":"https://pith.science/pith/6GZWCVFFASZ6GIOXFBWMDDBVED","download_json":"https://pith.science/pith/6GZWCVFFASZ6GIOXFBWMDDBVED.json","view_paper":"https://pith.science/paper/6GZWCVFF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.05816&json=true","fetch_graph":"https://pith.science/api/pith-number/6GZWCVFFASZ6GIOXFBWMDDBVED/graph.json","fetch_events":"https://pith.science/api/pith-number/6GZWCVFFASZ6GIOXFBWMDDBVED/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6GZWCVFFASZ6GIOXFBWMDDBVED/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6GZWCVFFASZ6GIOXFBWMDDBVED/action/storage_attestation","attest_author":"https://pith.science/pith/6GZWCVFFASZ6GIOXFBWMDDBVED/action/author_attestation","sign_citation":"https://pith.science/pith/6GZWCVFFASZ6GIOXFBWMDDBVED/action/citation_signature","submit_replication":"https://pith.science/pith/6GZWCVFFASZ6GIOXFBWMDDBVED/action/replication_record"}},"created_at":"2026-05-18T00:18:27.338420+00:00","updated_at":"2026-05-18T00:18:27.338420+00:00"}