Quantifying Influential Communities in Granular Social Networks Using Fuzzy Theory

Kumari, Anisha; Behera, Ranjan Kumar; Shukla, Abhishek Sai; Sahoo, Satya Prakash; Misra, Sanjay; Rath, Sanatanu Kumar

Community detection and centrality analysis in social networks are identified as pertinent research topics in the field of social network analysis. Community detection focuses on identifying the sub-graphs (communities) which have dense connections within it as compared to outside of it, whereas centrality analysis focuses on identifying significant nodes in a social network based on different aspects of importance. A number of research works have focused on identifying community structure in large-scale network. However, very less effort has been emphasized on quantifying the influence of the communities. In this paper, group of nodes that are likely to form communities are first uncovered and then they are quantified based on the influencing ability in the network. Identifying exact boundaries of communities are quite challenging in large scale network. The major contribution in this paper is to develop a model termed as FRC-FGSN (Fuzzy Rough Communities in Fuzzy Granular Social Network), to identify the communities with the help of fuzzy and rough set theory. The proposed model is based on a idea that, the degree of belongingness a node in a community may not be binary but can be models through fuzzy membership. The second contribution is to quantifying the influence of the community using eigenvector centrality. In order to improve the scalability, several steps in the proposed model have been implemented using map-reduce programming paradigm in a cluster-computing framework like Hadoop. Comparative analysis of FRC-FGSN with other parallel algorithms as available in the literature has been presented to demonstrate the scalability and effectiveness of the algorithm.