A Fast Fuzzy Clustering Algorithm for Complex Networks via a Generalized Momentum Method

Lun Hu, Xiangyu Pan, Zehai Tang, Xin Luo

Abstract: Complex networks have been widely adopted to represent a variety of complicated systems. Given a complex network, it is of great significance to perform accurate clustering for better understanding its intrinsic organization. To this end, a fuzzy-based clustering algorithm, i.e., FCAN, has been developed. Though effective, FCAN suffers from the disadvantage of slow convergence, which in return constrains its efficiency. To address this issue, this article proposes a fast fuzzy clustering algorithm, namely, F $^2$CAN, which incorporates a generalized momentum method into FCAN. Its fast convergence is rigorous justified in theory. Empirical studies on five datasets from real applications demonstrate that F $^2$CAN achieves a better performance when compared with FCAN and several state-of-the-art clustering algorithms in terms of convergence rate and clustering accuracy simultaneously. Hence, F $^2$CAN has potential for addressing the clustering analysis of large-scale complex networks emerging from industrial applications.