Centrality Approach for Community Detection in Social Network

Abstract

Social network comprise of social entities that are linked together with ties. The abundant use of social medias like Facebook, Instagram, Flicker, Youtube, Twitter, etc. leads to the evolution of more networks those are large, dynamic and complicated in nature. Social network can be represented as a graph structure where each node represents as an individual and each edge represents as a relation between the individuals. Community detection in social network plays a vital role in predicting the insights present in the complex network and hence is a very challenging task too. Community structure solves many real world problems by providing different solutions. Community is a collection of group of nodes where internal density of the edges is more and nodes are sparsely connected to the nodes of the other community. The nodes present inside a community exhibits similar kind properties and all are influenced by the central node of that particular community. Hence centrality detection mechanism is used to detect the central node of the network and further it is used to identify the communities present over the network. In order to minimize the computational time, MapReduce approach is adopted to determine the degree centrality values for each and every node. Finally communities are detected using identified central nodes. The results show that proposed method is more efficient in accuracy and time complexity as compared to other existing algorithms.