{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:52HWACH7C2DANFV4IKOP5E2JFP","short_pith_number":"pith:52HWACH7","schema_version":"1.0","canonical_sha256":"ee8f6008ff16860696bc429cfe93492be6067bda769dbaa4b23615ba4ba1a64e","source":{"kind":"arxiv","id":"1610.05347","version":1},"attestation_state":"computed","paper":{"title":"Link Prediction in evolving networks based on the popularity of nodes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.data-an","physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Ming-Yang Zhou, Tong Wang, Zhong-Qian Fu","submitted_at":"2016-09-12T07:48:30Z","abstract_excerpt":"Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict the missing edges or identify the spurious edges, and attracts much attention from various fields. The key issue of link prediction is to estimate the likelihood of two nodes in networks. Most current approaches of link prediction base on static structural analysis and ignore the temporal aspects of evolving networks. Unlike previous work, in this paper, we propose a popularity based structural perturbation method (PBSPM) that characterizes the similarity of an edge not only from exi"},"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":"1610.05347","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-09-12T07:48:30Z","cross_cats_sorted":["physics.data-an","physics.soc-ph"],"title_canon_sha256":"9a5726d340b1991a7293106f874ed0f1750f2d4c056e5434c4c96f8614a841da","abstract_canon_sha256":"80a8d55ba7de2b02dc89d9a1f25b7b27983f12075156c9be9ac86fdef7c8d2c3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:01:59.588179Z","signature_b64":"J8/K7+3RpgHSqcMK5d8IBMDSpUUjnZhm5uH54v0s694zZ6Tgipzesp7ifflaQYz6tg0m1JAITrG95H5wthllAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee8f6008ff16860696bc429cfe93492be6067bda769dbaa4b23615ba4ba1a64e","last_reissued_at":"2026-05-18T01:01:59.587489Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:01:59.587489Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Link Prediction in evolving networks based on the popularity of nodes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.data-an","physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Ming-Yang Zhou, Tong Wang, Zhong-Qian Fu","submitted_at":"2016-09-12T07:48:30Z","abstract_excerpt":"Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict the missing edges or identify the spurious edges, and attracts much attention from various fields. The key issue of link prediction is to estimate the likelihood of two nodes in networks. Most current approaches of link prediction base on static structural analysis and ignore the temporal aspects of evolving networks. Unlike previous work, in this paper, we propose a popularity based structural perturbation method (PBSPM) that characterizes the similarity of an edge not only from exi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.05347","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":"1610.05347","created_at":"2026-05-18T01:01:59.587571+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.05347v1","created_at":"2026-05-18T01:01:59.587571+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.05347","created_at":"2026-05-18T01:01:59.587571+00:00"},{"alias_kind":"pith_short_12","alias_value":"52HWACH7C2DA","created_at":"2026-05-18T12:29:58.707656+00:00"},{"alias_kind":"pith_short_16","alias_value":"52HWACH7C2DANFV4","created_at":"2026-05-18T12:29:58.707656+00:00"},{"alias_kind":"pith_short_8","alias_value":"52HWACH7","created_at":"2026-05-18T12:29:58.707656+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/52HWACH7C2DANFV4IKOP5E2JFP","json":"https://pith.science/pith/52HWACH7C2DANFV4IKOP5E2JFP.json","graph_json":"https://pith.science/api/pith-number/52HWACH7C2DANFV4IKOP5E2JFP/graph.json","events_json":"https://pith.science/api/pith-number/52HWACH7C2DANFV4IKOP5E2JFP/events.json","paper":"https://pith.science/paper/52HWACH7"},"agent_actions":{"view_html":"https://pith.science/pith/52HWACH7C2DANFV4IKOP5E2JFP","download_json":"https://pith.science/pith/52HWACH7C2DANFV4IKOP5E2JFP.json","view_paper":"https://pith.science/paper/52HWACH7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.05347&json=true","fetch_graph":"https://pith.science/api/pith-number/52HWACH7C2DANFV4IKOP5E2JFP/graph.json","fetch_events":"https://pith.science/api/pith-number/52HWACH7C2DANFV4IKOP5E2JFP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/52HWACH7C2DANFV4IKOP5E2JFP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/52HWACH7C2DANFV4IKOP5E2JFP/action/storage_attestation","attest_author":"https://pith.science/pith/52HWACH7C2DANFV4IKOP5E2JFP/action/author_attestation","sign_citation":"https://pith.science/pith/52HWACH7C2DANFV4IKOP5E2JFP/action/citation_signature","submit_replication":"https://pith.science/pith/52HWACH7C2DANFV4IKOP5E2JFP/action/replication_record"}},"created_at":"2026-05-18T01:01:59.587571+00:00","updated_at":"2026-05-18T01:01:59.587571+00:00"}