{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:RIMTE2DNQMJZ3PNLX5BY5C4BUJ","short_pith_number":"pith:RIMTE2DN","schema_version":"1.0","canonical_sha256":"8a1932686d83139dbdabbf438e8b81a25a33df89b25b8f87d32603b49023c5da","source":{"kind":"arxiv","id":"1802.08888","version":1},"attestation_state":"computed","paper":{"title":"N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Amol Kapoor, Bryan Perozzi, Joonseok Lee, Sami Abu-el-haija","submitted_at":"2018-02-24T18:30:30Z","abstract_excerpt":"Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data. Concurrently, unsupervised learning of graph embeddings has benefited from the information contained in random walks. In this paper, we propose a model: Network of GCNs (N-GCN), which marries these two lines of work. At its core, N-GCN trains multiple instances of GCNs over node pairs discovered at different distances in random walks, and learns a combination of the instance outputs which optimizes the classification objective. Our experiments show that our proposed N-G"},"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":"1802.08888","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-24T18:30:30Z","cross_cats_sorted":["cs.SI","stat.ML"],"title_canon_sha256":"6a9d7604fb0765fe91a8c9a591f23d56c4fb9e7e9c66bc7e5d07de0726614cc3","abstract_canon_sha256":"a86dca9e08cddab6136f9b4775a5a8066dae9d40ac6171cb2705eb402e197fc4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:36.146948Z","signature_b64":"OGCHHXZtWjn4YTZEH7LGDDf61PgG0JT+nGVIQylRi8nBrvuh9tn3LIxPG4BOycxJlQ4G0jwGJ/Z9F6Qr5BNBDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a1932686d83139dbdabbf438e8b81a25a33df89b25b8f87d32603b49023c5da","last_reissued_at":"2026-05-18T00:22:36.146266Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:36.146266Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Amol Kapoor, Bryan Perozzi, Joonseok Lee, Sami Abu-el-haija","submitted_at":"2018-02-24T18:30:30Z","abstract_excerpt":"Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data. Concurrently, unsupervised learning of graph embeddings has benefited from the information contained in random walks. In this paper, we propose a model: Network of GCNs (N-GCN), which marries these two lines of work. At its core, N-GCN trains multiple instances of GCNs over node pairs discovered at different distances in random walks, and learns a combination of the instance outputs which optimizes the classification objective. Our experiments show that our proposed N-G"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.08888","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":"1802.08888","created_at":"2026-05-18T00:22:36.146394+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.08888v1","created_at":"2026-05-18T00:22:36.146394+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.08888","created_at":"2026-05-18T00:22:36.146394+00:00"},{"alias_kind":"pith_short_12","alias_value":"RIMTE2DNQMJZ","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"RIMTE2DNQMJZ3PNL","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"RIMTE2DN","created_at":"2026-05-18T12:32:50.500415+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/RIMTE2DNQMJZ3PNLX5BY5C4BUJ","json":"https://pith.science/pith/RIMTE2DNQMJZ3PNLX5BY5C4BUJ.json","graph_json":"https://pith.science/api/pith-number/RIMTE2DNQMJZ3PNLX5BY5C4BUJ/graph.json","events_json":"https://pith.science/api/pith-number/RIMTE2DNQMJZ3PNLX5BY5C4BUJ/events.json","paper":"https://pith.science/paper/RIMTE2DN"},"agent_actions":{"view_html":"https://pith.science/pith/RIMTE2DNQMJZ3PNLX5BY5C4BUJ","download_json":"https://pith.science/pith/RIMTE2DNQMJZ3PNLX5BY5C4BUJ.json","view_paper":"https://pith.science/paper/RIMTE2DN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.08888&json=true","fetch_graph":"https://pith.science/api/pith-number/RIMTE2DNQMJZ3PNLX5BY5C4BUJ/graph.json","fetch_events":"https://pith.science/api/pith-number/RIMTE2DNQMJZ3PNLX5BY5C4BUJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RIMTE2DNQMJZ3PNLX5BY5C4BUJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RIMTE2DNQMJZ3PNLX5BY5C4BUJ/action/storage_attestation","attest_author":"https://pith.science/pith/RIMTE2DNQMJZ3PNLX5BY5C4BUJ/action/author_attestation","sign_citation":"https://pith.science/pith/RIMTE2DNQMJZ3PNLX5BY5C4BUJ/action/citation_signature","submit_replication":"https://pith.science/pith/RIMTE2DNQMJZ3PNLX5BY5C4BUJ/action/replication_record"}},"created_at":"2026-05-18T00:22:36.146394+00:00","updated_at":"2026-05-18T00:22:36.146394+00:00"}