{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:5YVW36ZOOD4UZD3J7SAEOUEXZM","short_pith_number":"pith:5YVW36ZO","schema_version":"1.0","canonical_sha256":"ee2b6dfb2e70f94c8f69fc80475097cb017e9fcca809bf9c4165160c507ad2a2","source":{"kind":"arxiv","id":"1807.03733","version":2},"attestation_state":"computed","paper":{"title":"Network Classification in Temporal Networks Using Motifs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SI","authors_text":"Dave Braines, Don Towsley, Jian Li, Kun Tu, Liam D. Turner","submitted_at":"2018-07-10T16:09:29Z","abstract_excerpt":"Network classification has a variety of applications, such as detecting communities within networks and finding similarities between those representing different aspects of the real world. However, most existing work in this area focus on examining static undirected networks without considering directed edges or temporality. In this paper, we propose a new methodology that utilizes feature representation for network classification based on the temporal motif distribution of the network and a null model for comparing against random graphs. Experimental results show that our method improves accu"},"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":"1807.03733","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-07-10T16:09:29Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f594f54ed844fb9c7660a8145d52de2e4297032b50d1d0372323b3c2dbd406b6","abstract_canon_sha256":"1e0db281ff392e6649c626229cd16ee03cae01bb907a5e12410222d472981bcd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:38.204386Z","signature_b64":"88GkaQ175WcmlwG7UP+xRYY06HjhemjU1xqcHIqus1WZeWhyAKhnDhLwCNXjtJgO66SHjlR/TYrxNKL4rZNCDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee2b6dfb2e70f94c8f69fc80475097cb017e9fcca809bf9c4165160c507ad2a2","last_reissued_at":"2026-05-18T00:08:38.203724Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:38.203724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Network Classification in Temporal Networks Using Motifs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SI","authors_text":"Dave Braines, Don Towsley, Jian Li, Kun Tu, Liam D. Turner","submitted_at":"2018-07-10T16:09:29Z","abstract_excerpt":"Network classification has a variety of applications, such as detecting communities within networks and finding similarities between those representing different aspects of the real world. However, most existing work in this area focus on examining static undirected networks without considering directed edges or temporality. In this paper, we propose a new methodology that utilizes feature representation for network classification based on the temporal motif distribution of the network and a null model for comparing against random graphs. Experimental results show that our method improves accu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.03733","kind":"arxiv","version":2},"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":"1807.03733","created_at":"2026-05-18T00:08:38.203812+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.03733v2","created_at":"2026-05-18T00:08:38.203812+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.03733","created_at":"2026-05-18T00:08:38.203812+00:00"},{"alias_kind":"pith_short_12","alias_value":"5YVW36ZOOD4U","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"5YVW36ZOOD4UZD3J","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"5YVW36ZO","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/5YVW36ZOOD4UZD3J7SAEOUEXZM","json":"https://pith.science/pith/5YVW36ZOOD4UZD3J7SAEOUEXZM.json","graph_json":"https://pith.science/api/pith-number/5YVW36ZOOD4UZD3J7SAEOUEXZM/graph.json","events_json":"https://pith.science/api/pith-number/5YVW36ZOOD4UZD3J7SAEOUEXZM/events.json","paper":"https://pith.science/paper/5YVW36ZO"},"agent_actions":{"view_html":"https://pith.science/pith/5YVW36ZOOD4UZD3J7SAEOUEXZM","download_json":"https://pith.science/pith/5YVW36ZOOD4UZD3J7SAEOUEXZM.json","view_paper":"https://pith.science/paper/5YVW36ZO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.03733&json=true","fetch_graph":"https://pith.science/api/pith-number/5YVW36ZOOD4UZD3J7SAEOUEXZM/graph.json","fetch_events":"https://pith.science/api/pith-number/5YVW36ZOOD4UZD3J7SAEOUEXZM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5YVW36ZOOD4UZD3J7SAEOUEXZM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5YVW36ZOOD4UZD3J7SAEOUEXZM/action/storage_attestation","attest_author":"https://pith.science/pith/5YVW36ZOOD4UZD3J7SAEOUEXZM/action/author_attestation","sign_citation":"https://pith.science/pith/5YVW36ZOOD4UZD3J7SAEOUEXZM/action/citation_signature","submit_replication":"https://pith.science/pith/5YVW36ZOOD4UZD3J7SAEOUEXZM/action/replication_record"}},"created_at":"2026-05-18T00:08:38.203812+00:00","updated_at":"2026-05-18T00:08:38.203812+00:00"}