Detecting Community Structure in Complex Networks Using Bacterial Chemotaxis with Fuzzy C-means Clustering

Abstract

Identification of (overlapping) communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, the bacterial chemotaxis (BC) strategy is used to maximize the modularity of a network, associating with a dissimilarity-index-based and with a diffusion-distance-based fuzzy c-means clustering iterative procedure. The proposed algorithm outperforms most existing methods in the literature as regards the optimal modularity found. Experimental results indicate that the new algorithm is efficient at detecting both good clusterings and the appropriate number of clusters.

Keywords

• Complex networks
• Community structure
• Modularity
• Bacterial Chemotaxis
• Fuzzy c-means clustering.