{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,9]],"date-time":"2024-11-09T05:02:50Z","timestamp":1731128570075,"version":"3.28.0"},"reference-count":39,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T00:00:00Z","timestamp":1728000000000},"content-version":"vor","delay-in-days":3,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["2024\u2010BS\u2010267"],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistical Analysis"],"published-print":{"date-parts":[[2024,10]]},"abstract":"ABSTRACT<\/jats:title>Central nodes, also known as vital or critical nodes, play a unique role in networks of various domains. To date, numerous centrality evaluation methods have been proposed to identify central nodes from different types of networks. However, most of existing methods did not address this issue from a rigorous significance testing perspective. As a result, it is hard for us to judge whether an identified central node is true or not and control the quality of reported nodes in a statistically sound manner. In this paper, we propose a centrality evaluation method based on hypothesis testing, where the test statistic is the degree sum of the neighbors of the node. We further derive an analytical p<\/jats:italic> value and its upper bound under the Erd\u00f6s\u2013R\u00e9nyi model, which can be employed as the centrality score for vital node recognition. To demonstrate the effectiveness of our method, extensive experiments are conducted on both simulated and real networks. The experimental results show that (1) our testing\u2010based centrality measure in terms of p<\/jats:italic> value is capable of distinguishing central nodes from non\u2010central ones effectively and (2) our method is able to identify central nodes more accurately than those classic centrality measures and state\u2010of\u2010the\u2010art (SOTA) centrality evaluation methods.<\/jats:p>","DOI":"10.1002\/sam.11712","type":"journal-article","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T10:40:07Z","timestamp":1728038407000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Node Centrality Inference via Hypothesis Testing"],"prefix":"10.1002","volume":"17","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-9526-8816","authenticated-orcid":false,"given":"Zengyou","family":"He","sequence":"first","affiliation":[{"name":"School of Software Dalian University of Technology Dalian China"},{"name":"Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian China"}]},{"given":"Jun","family":"Lou","sequence":"additional","affiliation":[{"name":"School of Software Dalian University of Technology Dalian China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1386-812X","authenticated-orcid":false,"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software Engineering Dalian University Dalian China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7470-9395","authenticated-orcid":false,"given":"Lianyu","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Software Dalian University of Technology Dalian China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9474-8375","authenticated-orcid":false,"given":"Mudi","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Software Dalian University of Technology Dalian China"}]}],"member":"311","published-online":{"date-parts":[[2024,10,4]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_10_2_1","DOI":"10.1353\/mpq.0.0014"},{"doi-asserted-by":"crossref","unstructured":"P.DomingosandM.Richardson \u201cMining the Network Value of Customers \u201dinProceedings of the Seventh International Conference on Knowledge Discovery and Data Mining ed.F.Provost(New York: ACM Press 2001) 57\u201366.","key":"e_1_2_10_3_1","DOI":"10.1145\/502512.502525"},{"doi-asserted-by":"publisher","key":"e_1_2_10_4_1","DOI":"10.1016\/j.physa.2013.06.059"},{"doi-asserted-by":"publisher","key":"e_1_2_10_5_1","DOI":"10.1038\/nphys1746"},{"doi-asserted-by":"publisher","key":"e_1_2_10_6_1","DOI":"10.1176\/appi.ajp.2012.11081289"},{"doi-asserted-by":"publisher","key":"e_1_2_10_7_1","DOI":"10.1103\/PhysRevE.75.036105"},{"doi-asserted-by":"publisher","key":"e_1_2_10_8_1","DOI":"10.1103\/PhysRevE.69.025103"},{"doi-asserted-by":"publisher","key":"e_1_2_10_9_1","DOI":"10.1360\/972013-1280"},{"doi-asserted-by":"publisher","key":"e_1_2_10_10_1","DOI":"10.1016\/j.physrep.2016.06.007"},{"issue":"1","key":"e_1_2_10_11_1","first-page":"16","article-title":"Analysis of Online Social Network Connections for Identification of Influential Users: Survey and Open Research Issues","volume":"51","author":"Al Garadi M. 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