{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:3BAEMBZPIF4NRTHI7R2SLQWHEN","short_pith_number":"pith:3BAEMBZP","schema_version":"1.0","canonical_sha256":"d84046072f4178d8cce8fc7525c2c7237c46d6dce4f4eb8b828fdd58f3a78cee","source":{"kind":"arxiv","id":"1908.10697","version":4},"attestation_state":"computed","paper":{"title":"Initialization for Network Embedding: A Graph Partition Approach","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SI","authors_text":"Faqiang Zhang, Feng He, Hongyun Cai, Wenqing Lin, Xu Cheng","submitted_at":"2019-08-28T12:53:01Z","abstract_excerpt":"Network embedding has been intensively studied in the literature and widely used in various applications, such as link prediction and node classification. While previous work focus on the design of new algorithms or are tailored for various problem settings, the discussion of initialization strategies in the learning process is often missed. In this work, we address this important issue of initialization for network embedding that could dramatically improve the performance of the algorithms on both effectiveness and efficiency. Specifically, we first exploit the graph partition technique that "},"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":"1908.10697","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.SI","submitted_at":"2019-08-28T12:53:01Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"035da5848fe8bb12701d9143e3c69c6dccfbc3dc9c28892c0702072d3c6f9003","abstract_canon_sha256":"80d254279b09df0ecca0bde853d3f222415ff74abe5f80c46383203c5dcebe7c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:18:09.955122Z","signature_b64":"Ff9gf0u4xQiRYWlYgGGLUGq7v3BnSeVK9NFIxM3EyVJhe5TWHrl7uGACn4qwT2vq2IYFVAfd2uieYbKQgQ3hBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d84046072f4178d8cce8fc7525c2c7237c46d6dce4f4eb8b828fdd58f3a78cee","last_reissued_at":"2026-07-05T00:18:09.954747Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:18:09.954747Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Initialization for Network Embedding: A Graph Partition Approach","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SI","authors_text":"Faqiang Zhang, Feng He, Hongyun Cai, Wenqing Lin, Xu Cheng","submitted_at":"2019-08-28T12:53:01Z","abstract_excerpt":"Network embedding has been intensively studied in the literature and widely used in various applications, such as link prediction and node classification. While previous work focus on the design of new algorithms or are tailored for various problem settings, the discussion of initialization strategies in the learning process is often missed. In this work, we address this important issue of initialization for network embedding that could dramatically improve the performance of the algorithms on both effectiveness and efficiency. Specifically, we first exploit the graph partition technique that "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.10697","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.10697/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"1908.10697","created_at":"2026-07-05T00:18:09.954801+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.10697v4","created_at":"2026-07-05T00:18:09.954801+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.10697","created_at":"2026-07-05T00:18:09.954801+00:00"},{"alias_kind":"pith_short_12","alias_value":"3BAEMBZPIF4N","created_at":"2026-07-05T00:18:09.954801+00:00"},{"alias_kind":"pith_short_16","alias_value":"3BAEMBZPIF4NRTHI","created_at":"2026-07-05T00:18:09.954801+00:00"},{"alias_kind":"pith_short_8","alias_value":"3BAEMBZP","created_at":"2026-07-05T00:18:09.954801+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/3BAEMBZPIF4NRTHI7R2SLQWHEN","json":"https://pith.science/pith/3BAEMBZPIF4NRTHI7R2SLQWHEN.json","graph_json":"https://pith.science/api/pith-number/3BAEMBZPIF4NRTHI7R2SLQWHEN/graph.json","events_json":"https://pith.science/api/pith-number/3BAEMBZPIF4NRTHI7R2SLQWHEN/events.json","paper":"https://pith.science/paper/3BAEMBZP"},"agent_actions":{"view_html":"https://pith.science/pith/3BAEMBZPIF4NRTHI7R2SLQWHEN","download_json":"https://pith.science/pith/3BAEMBZPIF4NRTHI7R2SLQWHEN.json","view_paper":"https://pith.science/paper/3BAEMBZP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.10697&json=true","fetch_graph":"https://pith.science/api/pith-number/3BAEMBZPIF4NRTHI7R2SLQWHEN/graph.json","fetch_events":"https://pith.science/api/pith-number/3BAEMBZPIF4NRTHI7R2SLQWHEN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3BAEMBZPIF4NRTHI7R2SLQWHEN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3BAEMBZPIF4NRTHI7R2SLQWHEN/action/storage_attestation","attest_author":"https://pith.science/pith/3BAEMBZPIF4NRTHI7R2SLQWHEN/action/author_attestation","sign_citation":"https://pith.science/pith/3BAEMBZPIF4NRTHI7R2SLQWHEN/action/citation_signature","submit_replication":"https://pith.science/pith/3BAEMBZPIF4NRTHI7R2SLQWHEN/action/replication_record"}},"created_at":"2026-07-05T00:18:09.954801+00:00","updated_at":"2026-07-05T00:18:09.954801+00:00"}