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Geodesic based centrality: Unifying the local and the global
Institution:1. Surrey Business School, University of Surrey, GU2 7XH, Guildford, UK;2. LINKS Center for Social Network Analysis, Gatton College of Business and Economics, University of Kentucky, Lexington, KY 40508, USA;3. Mitchell Centre for Social Network Analysis, School of Social Sciences, University of Manchester, Arthur Lewis Building, Bridgeford Street, M13 9PL, Manchester, UK;1. Headquarters, Department of the Army, United States Army, United States;2. Department of Statistics, George Mason University, United States;1. Educational Effectiveness and Evaluation, KU Leuven, Dekenstraat 2, Post Box 3773, 3000 Leuven, Belgium;2. Methodology of Educational Sciences, KU Leuven, Tiensestraat 102, Post Box 7654, 3000 Leuven, Belgium;3. Professional Learning & Development, Corporate Training and Lifelong Learning, KU Leuven, Dekenstraat 2, Post Box 3772, 3000 Leuven, Belgium;1. Stanford University, Department of Sociology, 450 Serra Mall, Building 120, Room 160, Stanford, CA 94305, United States;2. Stanford University, Computer Science Department, 353 Serra Mall, Stanford, CA 94305, United States
Abstract:A variety of node-level centrality measures, including purely structural measures (such as degree and closeness centrality) and measures incorporating characteristics of actors (such as the Blau's measure of heterogeneity) have been developed to measure a person's access to resources held by others. Each of these node-level measures can be placed on a continuum depending on whether they focus only on ego's direct contacts (e.g. degree centrality and Blau's measure of heterogeneity), or whether they also incorporate connections to others at longer distances in the network (e.g. closeness centrality or betweenness centrality). In this paper we propose generalized measures, where a tuning parameter δ regulates the relative impact of resources held by more close versus more distant others. We first show how, when a specific δ is chosen degree-centrality and reciprocal closeness centrality are two specific instances of this more general measure. We then demonstrate how a similar approach can be applied to node-level measures that incorporate attributes. When more or less weight is given to other nodes at longer distances with specific characteristics, a generalized measure of resource-richness and a generalized measure for diversity among one's connections can be obtained (following Blau's measure of heterogeneity). Finally, we show how this approach can also be applied to betweenness centrality to focus on more local (ego) betweenness or global (Freeman) betweenness. The importance of the choice of δ is illustrated on some classic network datasets.
Keywords:Degree centrality  Closeness centrality  Betweenness centrality  Diversity  Resource-richness  Node-level measures
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