{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:3HAWTN3PYX2Z2A6ARFUMWD2TUZ","short_pith_number":"pith:3HAWTN3P","schema_version":"1.0","canonical_sha256":"d9c169b76fc5f59d03c08968cb0f53a67b4dc6d38bdf5b8ddd5b031c5c4cc9f1","source":{"kind":"arxiv","id":"1704.05743","version":1},"attestation_state":"computed","paper":{"title":"The Hidden Flow Structure and Metric Space of Network Embedding Algorithms Based on Random Walks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.soc-ph","authors_text":"Jiang Zhang, Li Gong, Weiwei Gu, Xiandao Lou","submitted_at":"2017-04-19T14:17:58Z","abstract_excerpt":"Network embedding which encodes all vertices in a network as a set of numerical vectors in accordance with it's local and global structures, has drawn widespread attention. Network embedding not only learns significant features of a network, such as the clustering and linking prediction but also learns the latent vector representation of the nodes which provides theoretical support for a variety of applications, such as visualization, node classification, and recommendation. As the latest progress of the research, several algorithms based on random walks have been devised. Although their high "},"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":"1704.05743","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.soc-ph","submitted_at":"2017-04-19T14:17:58Z","cross_cats_sorted":[],"title_canon_sha256":"b3dfd82423f3943d652938f9bf688dd01f31a9d4c1f76ed74a5e65681e0c13f7","abstract_canon_sha256":"ee7490d4f0e215697daeaf848b446736bf88259d1c15f231b102c20a91468bf8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:06.645091Z","signature_b64":"Xltvqu64JK55k3v4H8AEJZOdLS7LPmyy0MwxLkywSf3v+bmYAX0EXWpI8SpQdoCfyTc0SffGoUP5vT5dW3x8DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d9c169b76fc5f59d03c08968cb0f53a67b4dc6d38bdf5b8ddd5b031c5c4cc9f1","last_reissued_at":"2026-05-18T00:46:06.644672Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:06.644672Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Hidden Flow Structure and Metric Space of Network Embedding Algorithms Based on Random Walks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.soc-ph","authors_text":"Jiang Zhang, Li Gong, Weiwei Gu, Xiandao Lou","submitted_at":"2017-04-19T14:17:58Z","abstract_excerpt":"Network embedding which encodes all vertices in a network as a set of numerical vectors in accordance with it's local and global structures, has drawn widespread attention. Network embedding not only learns significant features of a network, such as the clustering and linking prediction but also learns the latent vector representation of the nodes which provides theoretical support for a variety of applications, such as visualization, node classification, and recommendation. As the latest progress of the research, several algorithms based on random walks have been devised. Although their high "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.05743","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":"1704.05743","created_at":"2026-05-18T00:46:06.644740+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.05743v1","created_at":"2026-05-18T00:46:06.644740+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.05743","created_at":"2026-05-18T00:46:06.644740+00:00"},{"alias_kind":"pith_short_12","alias_value":"3HAWTN3PYX2Z","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"3HAWTN3PYX2Z2A6A","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"3HAWTN3P","created_at":"2026-05-18T12:30:58.224056+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/3HAWTN3PYX2Z2A6ARFUMWD2TUZ","json":"https://pith.science/pith/3HAWTN3PYX2Z2A6ARFUMWD2TUZ.json","graph_json":"https://pith.science/api/pith-number/3HAWTN3PYX2Z2A6ARFUMWD2TUZ/graph.json","events_json":"https://pith.science/api/pith-number/3HAWTN3PYX2Z2A6ARFUMWD2TUZ/events.json","paper":"https://pith.science/paper/3HAWTN3P"},"agent_actions":{"view_html":"https://pith.science/pith/3HAWTN3PYX2Z2A6ARFUMWD2TUZ","download_json":"https://pith.science/pith/3HAWTN3PYX2Z2A6ARFUMWD2TUZ.json","view_paper":"https://pith.science/paper/3HAWTN3P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.05743&json=true","fetch_graph":"https://pith.science/api/pith-number/3HAWTN3PYX2Z2A6ARFUMWD2TUZ/graph.json","fetch_events":"https://pith.science/api/pith-number/3HAWTN3PYX2Z2A6ARFUMWD2TUZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3HAWTN3PYX2Z2A6ARFUMWD2TUZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3HAWTN3PYX2Z2A6ARFUMWD2TUZ/action/storage_attestation","attest_author":"https://pith.science/pith/3HAWTN3PYX2Z2A6ARFUMWD2TUZ/action/author_attestation","sign_citation":"https://pith.science/pith/3HAWTN3PYX2Z2A6ARFUMWD2TUZ/action/citation_signature","submit_replication":"https://pith.science/pith/3HAWTN3PYX2Z2A6ARFUMWD2TUZ/action/replication_record"}},"created_at":"2026-05-18T00:46:06.644740+00:00","updated_at":"2026-05-18T00:46:06.644740+00:00"}