Multiple perspective centrality measures based on facility location problem under inter-group competitive environment
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Applied Network Science
Open Access
RESEARCH
Multiple perspective centrality measures based on facility location problem under inter‑group competitive environment Takayasu Fushimi1* , Seiya Okubo2 and Kazumi Saito3,4 *Correspondence: [email protected] 1 School of Computer Science, Tokyo University of Technology, 1404‑1 Katakuramachi, Hachioji City, Tokyo 192‑0982, Japan Full list of author information is available at the end of the article
Abstract In this study, we propose novel centrality measures considering multiple perspectives of nodes or node groups based on the facility location problem on a spatial network. The conventional centrality exclusively quantifies the global properties of each node in a network such as closeness and betweenness, and extracts nodes with high scores as important nodes. In the context of facility placement on a network, it is desirable to place facilities at nodes with high accessibility from residents, that is, nodes with a high score in closeness centrality. It is natural to think that such a property of a node changes when the situation changes. For example, in a situation where there are no existing facilities, it is expected that the demand of residents will be satisfied by opening a new facility at the node with the highest accessibility, however, in a situation where there exist some facilities, it is necessary to open a new facility some distance from the existing facilities. Furthermore, it is natural to consider that the concept of closeness differs depending on the relationship with existing facilities, cooperative relationships and competitive relationships. Therefore, we extend a concept of centrality so as to considers the situation where one or more nodes have already been selected belonging to one of some groups. In this study, we propose two measures based on closeness centrality and betweenness centrality as behavior models of people on a spatial network. From our experimental evaluations using actual urban street network data, we confirm that the proposed method, which introduces the viewpoints of each group, shows that there is a difference in the important nodes of each group viewpoint, and that the new store location can be predicted more accurately. Keywords: Centrality measure, Multiple perspectives, Facility location problem, Intergroup competition, Spatial network
Introduction In recent years, networks have been widely observed around us, and the technology of network science has been applied to various real-world problems. As a typical method of social network analysis, there is a centrality measure that extracts important nodes from a large amount of nodes constituting a network. The conventional centrality exclusively quantifies the global properties of each node in a network such as closeness and betweenness, and extracts nodes with high scores as important © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and repr
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