{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:5NUXQQQCR3VS6CVA2TANY7NNGK","short_pith_number":"pith:5NUXQQQC","schema_version":"1.0","canonical_sha256":"eb697842028eeb2f0aa0d4c0dc7dad32b1d4a88318b994b103e1b35c4eb4bed3","source":{"kind":"arxiv","id":"2005.02151","version":3},"attestation_state":"computed","paper":{"title":"Vertex Nomination in Richly Attributed Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.ML","stat.TH"],"primary_cat":"cs.IR","authors_text":"Carey E. Priebe, Keith Levin, Vince Lyzinski","submitted_at":"2020-04-29T15:13:24Z","abstract_excerpt":"Vertex nomination is a lightly-supervised network information retrieval task in which vertices of interest in one graph are used to query a second graph to discover vertices of interest in the second graph. Similar to other information retrieval tasks, the output of a vertex nomination scheme is a ranked list of the vertices in the second graph, with the heretofore unknown vertices of interest ideally concentrating at the top of the list. Vertex nomination schemes provide a useful suite of tools for efficiently mining complex networks for pertinent information. In this paper, we explore, both "},"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":"2005.02151","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-04-29T15:13:24Z","cross_cats_sorted":["cs.LG","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"b5bc7dc593f185ff29f0e89e10a57f9e63edb656713a63b239b6e3b8cbceacc9","abstract_canon_sha256":"6f22ce1b7c4627cdcb5ec4291c9526da09f43edd655bdd91d861fb3b22b71239"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:06:50.950994Z","signature_b64":"lsNNhai5Djyln8LbImIHaScSENu7Tk6LFCp6lTDoY7JXnPcZZxO1yziIxQY0CrO0ZNMMQ8YUhL6W+eCxU1TGDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb697842028eeb2f0aa0d4c0dc7dad32b1d4a88318b994b103e1b35c4eb4bed3","last_reissued_at":"2026-07-05T06:06:50.950594Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:06:50.950594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Vertex Nomination in Richly Attributed Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.ML","stat.TH"],"primary_cat":"cs.IR","authors_text":"Carey E. Priebe, Keith Levin, Vince Lyzinski","submitted_at":"2020-04-29T15:13:24Z","abstract_excerpt":"Vertex nomination is a lightly-supervised network information retrieval task in which vertices of interest in one graph are used to query a second graph to discover vertices of interest in the second graph. Similar to other information retrieval tasks, the output of a vertex nomination scheme is a ranked list of the vertices in the second graph, with the heretofore unknown vertices of interest ideally concentrating at the top of the list. Vertex nomination schemes provide a useful suite of tools for efficiently mining complex networks for pertinent information. In this paper, we explore, both "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.02151","kind":"arxiv","version":3},"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/2005.02151/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":"2005.02151","created_at":"2026-07-05T06:06:50.950657+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.02151v3","created_at":"2026-07-05T06:06:50.950657+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.02151","created_at":"2026-07-05T06:06:50.950657+00:00"},{"alias_kind":"pith_short_12","alias_value":"5NUXQQQCR3VS","created_at":"2026-07-05T06:06:50.950657+00:00"},{"alias_kind":"pith_short_16","alias_value":"5NUXQQQCR3VS6CVA","created_at":"2026-07-05T06:06:50.950657+00:00"},{"alias_kind":"pith_short_8","alias_value":"5NUXQQQC","created_at":"2026-07-05T06:06:50.950657+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.03772","citing_title":"Testing for correlation between network structure and high-dimensional node covariates","ref_index":54,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5NUXQQQCR3VS6CVA2TANY7NNGK","json":"https://pith.science/pith/5NUXQQQCR3VS6CVA2TANY7NNGK.json","graph_json":"https://pith.science/api/pith-number/5NUXQQQCR3VS6CVA2TANY7NNGK/graph.json","events_json":"https://pith.science/api/pith-number/5NUXQQQCR3VS6CVA2TANY7NNGK/events.json","paper":"https://pith.science/paper/5NUXQQQC"},"agent_actions":{"view_html":"https://pith.science/pith/5NUXQQQCR3VS6CVA2TANY7NNGK","download_json":"https://pith.science/pith/5NUXQQQCR3VS6CVA2TANY7NNGK.json","view_paper":"https://pith.science/paper/5NUXQQQC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.02151&json=true","fetch_graph":"https://pith.science/api/pith-number/5NUXQQQCR3VS6CVA2TANY7NNGK/graph.json","fetch_events":"https://pith.science/api/pith-number/5NUXQQQCR3VS6CVA2TANY7NNGK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5NUXQQQCR3VS6CVA2TANY7NNGK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5NUXQQQCR3VS6CVA2TANY7NNGK/action/storage_attestation","attest_author":"https://pith.science/pith/5NUXQQQCR3VS6CVA2TANY7NNGK/action/author_attestation","sign_citation":"https://pith.science/pith/5NUXQQQCR3VS6CVA2TANY7NNGK/action/citation_signature","submit_replication":"https://pith.science/pith/5NUXQQQCR3VS6CVA2TANY7NNGK/action/replication_record"}},"created_at":"2026-07-05T06:06:50.950657+00:00","updated_at":"2026-07-05T06:06:50.950657+00:00"}