Abstract
Community detection in social networks plays a vital role. The understanding and detection of communities in social networks is a challenging research problem. There exist many methods for detecting communities in large scale networks. Most of these methods presume the predefined number of communities and apply detection methods to exactly find out the predefined number of communities. However, there may not be the predefined number of communities naturally occurring in the social networks. Application of brute force inorder to predefine the number of communities goes against the natural occurrence of communities in the networks. In this paper, we propose a method for community detection which explores Self Organizing Maps for natural cluster selection and modularity measure for community strength identification. Experimental results on the real world network datasets show the effectiveness of the proposed approach.