A novel attributed community detection by integration of feature weighting and node centrality

Abstract

Community detection is one of the primary problems in social network analysis and this problem has more challenges in attributed social networks. The purpose of community detection in attributed social networks is to discover communities with not only homogeneous node properties but also adherent structures. Although community detection has been extensively studied, attributed community detection of large social networks with a large number of attributes remains a vital challenge. To address this challenge, in this paper a novel attributed community detection method is developed by integration of feature weighting with node centrality techniques. The developed method includes two main phases: (1) Weight Matrix Calculation, (2) Label Propagation Algorithm-based Attributed Community Detection. The aim of the first phase is to calculate the weight between two linked nodes using structural and attribute similarities, while, in the second phase, an improved label propagation algorithm-based community detection method in the attributed social network is proposed. The purpose of the second phase is to detect different communities by employing the calculated weight matrix and node popularity. After implementing the proposed method, its performance is compared with several other state of the art methods using some benchmarked real-world datasets. The results indicate that the developed method outperforms several other state-of-the-art methods and ascertain the effectiveness of the developed method for attributed community detection.