Physica A 367 (2006) 577–585 The analysis and dissimilarity comparison of community structure Peng Zhang a , Menghui Li a , Jinshan Wu b , Zengru Di a , Ying Fan a,Ã a Department of Systems Science, School of Management, Beijing Normal University, Beijing 100875, PR China b Department of Physics & Astronomy, University of British Columbia, Vancouver, B.C. Canada V6T 1Z1 Received 1 August 2005; received in revised form 1 November 2005 Available online 13 December 2005 Abstract Based on a database of collaboration recording in econophysics scientists and other networks, hierarchical clustering method and the algorithm of Girvan and Newman are applied to detect their community structure. The interesting results for community structure of econophysicists collaboration network are shown. A dissimilarity function D is proposed to quantitatively measure the difference between community structures obtained by different methods. Using this measurement, the differences between the process and community results obtained by aforementioned algorithms are given. The effectiveness of hierarchical clustering method and GN algorithm for detecting community structure in various networks is discussed. r 2005 Elsevier B.V. All rights reserved. Keywords: Complex networks; Community structure; Dissimilarity; Weight 1. Introduction In recent years, as more and more systems in many different fields can be depicted as complex networks, the research in complex networks has been gradually becoming an important issue in the study of complexity [1–3]. A network is composed of a set of vertices and edges which represent the relationship between two nodes. Examples include WWW, internet, food webs, biochemical networks, social networks, and so on [4–10]. The research in networks helps us understand these systems, and raises new concepts and methods. As one of the important properties of networks, community structure attracts us much attention. Different metrics of strength of connection among vertices form the community structure. Community structure is the group of network vertices. Within group there are dense internal links among nodes, but between groups nodes are loosely connected to the rest of the network [11]. It is one of the most important characters to understand the functional properties of complex structures. Recent empirical studies on networks display that there are communities in most social and biological networks [11,12]. This finding is ARTICLE IN PRESS www.elsevier.com/locate/physa 0378-4371/$ - see front matter r 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.physa.2005.11.018 Ã Corresponding author. E-mail address: yfan@bnu.edu.cn (Y. Fan).