{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:I3T2XPLGHDINQSB3PDXUUVL3RQ","short_pith_number":"pith:I3T2XPLG","schema_version":"1.0","canonical_sha256":"46e7abbd6638d0d8483b78ef4a557b8c072cae2e606e5662862cd06bca7c458d","source":{"kind":"arxiv","id":"2011.07190","version":1},"attestation_state":"computed","paper":{"title":"Centrality Measures in Complex Networks: A Survey","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Akrati Saxena, Sudarshan Iyengar","submitted_at":"2020-11-14T01:55:11Z","abstract_excerpt":"In complex networks, each node has some unique characteristics that define the importance of the node based on the given application-specific context. These characteristics can be identified using various centrality metrics defined in the literature. Some of these centrality measures can be computed using local information of the node, such as degree centrality and semi-local centrality measure. Others use global information of the network like closeness centrality, betweenness centrality, eigenvector centrality, Katz centrality, PageRank, and so on. In this survey, we discuss these centrality"},"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":"2011.07190","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.SI","submitted_at":"2020-11-14T01:55:11Z","cross_cats_sorted":[],"title_canon_sha256":"ef31893b36438dff7ecf3b8148048b3f25fc5537b5d69b1ff158225ca3bc8520","abstract_canon_sha256":"2f0a1d09a3d26982a7bc582b245ceb42ff48c52bd6829748e80be68c24c5b327"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:51:29.888919Z","signature_b64":"lXDLq2DOLRb5ljDm7vxxWZ9Knv9ZSV6fexb6JKQDVcjVqtg3bFUiLipkHLh9nSo/WY7+ST6D+Ps4L61Tm3KhBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46e7abbd6638d0d8483b78ef4a557b8c072cae2e606e5662862cd06bca7c458d","last_reissued_at":"2026-07-05T01:51:29.888477Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:51:29.888477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Centrality Measures in Complex Networks: A Survey","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Akrati Saxena, Sudarshan Iyengar","submitted_at":"2020-11-14T01:55:11Z","abstract_excerpt":"In complex networks, each node has some unique characteristics that define the importance of the node based on the given application-specific context. These characteristics can be identified using various centrality metrics defined in the literature. Some of these centrality measures can be computed using local information of the node, such as degree centrality and semi-local centrality measure. Others use global information of the network like closeness centrality, betweenness centrality, eigenvector centrality, Katz centrality, PageRank, and so on. In this survey, we discuss these centrality"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.07190","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2011.07190/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":"2011.07190","created_at":"2026-07-05T01:51:29.888536+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.07190v1","created_at":"2026-07-05T01:51:29.888536+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.07190","created_at":"2026-07-05T01:51:29.888536+00:00"},{"alias_kind":"pith_short_12","alias_value":"I3T2XPLGHDIN","created_at":"2026-07-05T01:51:29.888536+00:00"},{"alias_kind":"pith_short_16","alias_value":"I3T2XPLGHDINQSB3","created_at":"2026-07-05T01:51:29.888536+00:00"},{"alias_kind":"pith_short_8","alias_value":"I3T2XPLG","created_at":"2026-07-05T01:51:29.888536+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24121","citing_title":"Sphere of Influence Centrality via Shapley Values: Empirical Approximation and Network Coverage Analysis","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2409.16163","citing_title":"The anonymization problem in social networks","ref_index":52,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I3T2XPLGHDINQSB3PDXUUVL3RQ","json":"https://pith.science/pith/I3T2XPLGHDINQSB3PDXUUVL3RQ.json","graph_json":"https://pith.science/api/pith-number/I3T2XPLGHDINQSB3PDXUUVL3RQ/graph.json","events_json":"https://pith.science/api/pith-number/I3T2XPLGHDINQSB3PDXUUVL3RQ/events.json","paper":"https://pith.science/paper/I3T2XPLG"},"agent_actions":{"view_html":"https://pith.science/pith/I3T2XPLGHDINQSB3PDXUUVL3RQ","download_json":"https://pith.science/pith/I3T2XPLGHDINQSB3PDXUUVL3RQ.json","view_paper":"https://pith.science/paper/I3T2XPLG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.07190&json=true","fetch_graph":"https://pith.science/api/pith-number/I3T2XPLGHDINQSB3PDXUUVL3RQ/graph.json","fetch_events":"https://pith.science/api/pith-number/I3T2XPLGHDINQSB3PDXUUVL3RQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I3T2XPLGHDINQSB3PDXUUVL3RQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I3T2XPLGHDINQSB3PDXUUVL3RQ/action/storage_attestation","attest_author":"https://pith.science/pith/I3T2XPLGHDINQSB3PDXUUVL3RQ/action/author_attestation","sign_citation":"https://pith.science/pith/I3T2XPLGHDINQSB3PDXUUVL3RQ/action/citation_signature","submit_replication":"https://pith.science/pith/I3T2XPLGHDINQSB3PDXUUVL3RQ/action/replication_record"}},"created_at":"2026-07-05T01:51:29.888536+00:00","updated_at":"2026-07-05T01:51:29.888536+00:00"}