On detecting communities in social networks with interests

Abstract

Social networks have gained a lot of interest in recent literature due to the huge amount of data that can be extracted from them. With this ever growing data, emerged the need for techniques to handle it and analyze it. Several papers have proposed many techniques to analyze a given social network from several aspects. Communities are a crucial property in social networks and community detection is considered one of the most important problems in these networks. For this, many papers have devised algorithms for detecting communities. The issue with these algorithms is that they only take into consideration the relation (or distance) between the nodes for detecting communities. In this paper, a new algorithm is proposed to detect communities based on the interests of the nodes rather than their distances from each other. The paper carries out some experiments to test how well is the clustering algorithm in terms of the accuracy and the execution time.