Social Networks in Emergency Response

Wang, Dashun; Lin, Yu‐Ru; Bagrow, James P.

doi:10.1007/978-1-4614-7163-9_293-1

Cited by 2 publications

(1 citation statement)

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“…Community detection is helpful to improve user-oriented services [4], visualize the structures of networks [5], response emergency and disaster [6], analysis importance of nodes [7], and other applications. At present, most existing community detection algorithms are designed to find densely connected groups of nodes from only one single graph in which nodes represent actors and edges indicate only a relatively homogenous relationship (such as friendship) amongst actors.…”

Section: Introductionmentioning

confidence: 99%

A Coalition Formation Game Theory-Based Approach for Detecting Communities in Multi-relational Networks

Zhou

Yang

Zhang

et al. 2015

Web-Age Information Management

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Abstract. Community detection is a very important task in social network analysis. Most existing community detection algorithms are designed for singlerelational networks. However, in the real world, social networks are mostly multi-relational. In this paper, we propose a coalition formation game theorybased approach to detect communities in multi-relational social networks. We define the multi-relational communities as the shared communities over multiple single-relational graphs, and model community detection as a coalition formation game process in which actors in a social network are modeled as rational players trying to improve group's utilities by cooperating with other players to form coalitions. Each player is allowed to join multiple coalitions and coalitions with fewer players can merge into a larger coalition as long as the merge operation could improve the utilities of coalitions merged. We then use a greedy agglomerative manner to identify communities. Experimental results and performance studies verify the effectiveness of our approach.

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Section: Introductionmentioning

confidence: 99%