{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:YV54L5I3POLMUCBKRLZFSGIF2A","short_pith_number":"pith:YV54L5I3","schema_version":"1.0","canonical_sha256":"c57bc5f51b7b96ca082a8af2591905d02dbe1e62b0c431d924012c42e0c3570b","source":{"kind":"arxiv","id":"1809.02394","version":1},"attestation_state":"computed","paper":{"title":"Deep Feature Learning of Multi-Network Topology for Node Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hansheng Xue, Jiajie Peng, Xuequn Shang","submitted_at":"2018-09-07T10:36:22Z","abstract_excerpt":"Networks are ubiquitous structure that describes complex relationships between different entities in the real world. As a critical component of prediction task over nodes in networks, learning the feature representation of nodes has become one of the most active areas recently. Network Embedding, aiming to learn non-linear and low-dimensional feature representation based on network topology, has been proved to be helpful on tasks of network analysis, especially node classification. For many real-world systems, multiple types of relations are naturally represented by multiple networks. However,"},"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":"1809.02394","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-07T10:36:22Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"2c2886259cfb9b06a82cf5499992c15645f2cb02affaf0f338e388f9657869a7","abstract_canon_sha256":"8296579d59e9bf5beb4217b1f350990f0c810eb3cab6d93289d3322ef41c4e6d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:17.237555Z","signature_b64":"lse2VvFcCx+E5KbXhyZcSbDRkj33UcFBguYcbIOzyi91LSWKf8j3ImxuZIo2xHBgR9IKe4YGOwsysSTAA/2SAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c57bc5f51b7b96ca082a8af2591905d02dbe1e62b0c431d924012c42e0c3570b","last_reissued_at":"2026-05-18T00:06:17.236989Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:17.236989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Feature Learning of Multi-Network Topology for Node Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hansheng Xue, Jiajie Peng, Xuequn Shang","submitted_at":"2018-09-07T10:36:22Z","abstract_excerpt":"Networks are ubiquitous structure that describes complex relationships between different entities in the real world. As a critical component of prediction task over nodes in networks, learning the feature representation of nodes has become one of the most active areas recently. Network Embedding, aiming to learn non-linear and low-dimensional feature representation based on network topology, has been proved to be helpful on tasks of network analysis, especially node classification. For many real-world systems, multiple types of relations are naturally represented by multiple networks. However,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.02394","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":"1809.02394","created_at":"2026-05-18T00:06:17.237071+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.02394v1","created_at":"2026-05-18T00:06:17.237071+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.02394","created_at":"2026-05-18T00:06:17.237071+00:00"},{"alias_kind":"pith_short_12","alias_value":"YV54L5I3POLM","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_16","alias_value":"YV54L5I3POLMUCBK","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_8","alias_value":"YV54L5I3","created_at":"2026-05-18T12:33:04.347982+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/YV54L5I3POLMUCBKRLZFSGIF2A","json":"https://pith.science/pith/YV54L5I3POLMUCBKRLZFSGIF2A.json","graph_json":"https://pith.science/api/pith-number/YV54L5I3POLMUCBKRLZFSGIF2A/graph.json","events_json":"https://pith.science/api/pith-number/YV54L5I3POLMUCBKRLZFSGIF2A/events.json","paper":"https://pith.science/paper/YV54L5I3"},"agent_actions":{"view_html":"https://pith.science/pith/YV54L5I3POLMUCBKRLZFSGIF2A","download_json":"https://pith.science/pith/YV54L5I3POLMUCBKRLZFSGIF2A.json","view_paper":"https://pith.science/paper/YV54L5I3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.02394&json=true","fetch_graph":"https://pith.science/api/pith-number/YV54L5I3POLMUCBKRLZFSGIF2A/graph.json","fetch_events":"https://pith.science/api/pith-number/YV54L5I3POLMUCBKRLZFSGIF2A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YV54L5I3POLMUCBKRLZFSGIF2A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YV54L5I3POLMUCBKRLZFSGIF2A/action/storage_attestation","attest_author":"https://pith.science/pith/YV54L5I3POLMUCBKRLZFSGIF2A/action/author_attestation","sign_citation":"https://pith.science/pith/YV54L5I3POLMUCBKRLZFSGIF2A/action/citation_signature","submit_replication":"https://pith.science/pith/YV54L5I3POLMUCBKRLZFSGIF2A/action/replication_record"}},"created_at":"2026-05-18T00:06:17.237071+00:00","updated_at":"2026-05-18T00:06:17.237071+00:00"}