Community detection is one of the fundamental problems in social network analysis. Community detection on an attributed social networks aims to discover communities that have not only adhesive structure but also homogeneous node properties. 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, a novel attributed community detection method through an integration of feature weighting with node centrality techniques is proposed in this seminar. The proposal includes two main phases: (1) Weight Matrix Calculation, (2) Label Propagation Algorithm-basedAttributed 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 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 proposed method outperforms several state-of-the-art methods and ascertain the effectiveness of the proposed method for attributed community detection.