{"title":"The interplay of structural features and observed dissimilarities among centrality indices","authors":"David Schoch , Termeh Shafie","doi":"10.1016/j.socnet.2023.11.006","DOIUrl":null,"url":null,"abstract":"<div><p>An abundance of centrality indices has been proposed which capture the importance of nodes in a network based on different structural features. While there remains a persistent belief that similarities in outcomes of indices is contingent on their technical definitions, a growing body of research shows that structural features affect observed similarities more than technicalities. We conduct a series of experiments on artificial networks to trace the influence of specific structural features on the similarity of indices which confirm previous results in the literature. Our analysis on 1163 real-world networks, however, shows that little of the observations on synthetic networks convincingly carry over to empirical settings. Our findings suggest that although it seems clear that (dis)similarities among centralities depend on structural properties of the network, using correlation type analyses do not seem to be a promising approach to uncover such connections.</p></div>","PeriodicalId":48353,"journal":{"name":"Social Networks","volume":"78 ","pages":"Pages 54-64"},"PeriodicalIF":2.9000,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378873323000825/pdfft?md5=011c82b0dde8e6c43f3652b51ad89f0f&pid=1-s2.0-S0378873323000825-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Social Networks","FirstCategoryId":"90","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378873323000825","RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ANTHROPOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
An abundance of centrality indices has been proposed which capture the importance of nodes in a network based on different structural features. While there remains a persistent belief that similarities in outcomes of indices is contingent on their technical definitions, a growing body of research shows that structural features affect observed similarities more than technicalities. We conduct a series of experiments on artificial networks to trace the influence of specific structural features on the similarity of indices which confirm previous results in the literature. Our analysis on 1163 real-world networks, however, shows that little of the observations on synthetic networks convincingly carry over to empirical settings. Our findings suggest that although it seems clear that (dis)similarities among centralities depend on structural properties of the network, using correlation type analyses do not seem to be a promising approach to uncover such connections.
期刊介绍：
Social Networks is an interdisciplinary and international quarterly. It provides a common forum for representatives of anthropology, sociology, history, social psychology, political science, human geography, biology, economics, communications science and other disciplines who share an interest in the study of the empirical structure of social relations and associations that may be expressed in network form. It publishes both theoretical and substantive papers. Critical reviews of major theoretical or methodological approaches using the notion of networks in the analysis of social behaviour are also included, as are reviews of recent books dealing with social networks and social structure.
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