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

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