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Title: Community detection using intelligent clustering technique and sub-matrix density ordering
Authors: Liang, Tianzhu
Szeto, Kwok-Yip
Keywords: Community detection
Node clustering
Energy minimization
Greedy algorithm
Sub-matrix density
Hierarchical structure
Issue Date: Jul-2009
Citation: The XIII International conference 'Applied Stochastic Models and Data Analysis' (ASMDA-2009) June 30-July 3, 2009, Vilnius, Lithuania, p. 245-249
Abstract: Detecting communities in real world networks is an important problem for data analysis in science and engineering. By clustering nodes intelligently, a recursive algorithm is designed to detect community. Since the relabeling of nodes does not alter the topology of the network, the problem of community detection corresponds to the finding of a good labelling of nodes so that the adjacency matrix form blocks. By putting a fictitious interaction between nodes, the relabeling problem becomes one of energy minimization, where the total energy of the network is defined by putting interaction between the labels of nodes so that the clustering of nodes in the same community will decrease the total energy. A greedy algorithm is used for the computation of minimum energy. The method shows efficient detection of community in artifical as well as real world network. The result is illustrated in a tree showing hierarchical structure of communities on the basis of sub-matrix density.
Rights: © Institute of Mathematics and Informatics, 2009; © Vilnius Gediminas Technical University, 2009.
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