FCAN-MOPSO: An Improved Fuzzy-Based Graph Clustering Algorithm for Complex Networks With Multiobjective Particle Swarm Optimization

Performing an accurate clustering analysis is of great significance for us to understand the behavior of complex networks, and a variety of graph clustering algorithms have, thus, been proposed to do so by taking into account network topology and node attributes. Among them, fuzzy clustering algorithm for complex networks (FCAN) is an established fuzzy clustering algorithm that optimizes the memberships of nodes based on dense structures and content relevance. This article proposes an improved fuzzy-based graph clustering algorithm, namely FCAN-multi objective particle swarm optimization (MOPSO) that retains all the benefits associated with FCAN while achieving significantly increased convergence rate using multiobjective particle swarm optimization (MOPSO). To do so, FCAN-MOPSO first modifies the original optimization model of FCAN by adopting an instance-frequency-weighted regularization, which enhances the ability of FCAN-MOPSO to handle the imbalance observed in the distribution of fuzzy memberships of nodes. After that, FCAN-MOPSO decomposes its optimization problem into a set of suboptimization problems. Following the MOPSO framework, FCAN-MOPSO develops an effective solution to reach a consensus optimization among them by balancing the global exploration and local exploitation abilities of particles. A theoretical analysis is provided to prove the global convergence of FCAN-MOPSO. Extensive experiments have been conducted to evaluate the performance of FCAN-MOPSO on five real-world complex networks with different scale, and experimental results demonstrate that when compared with state-of-the-art clustering algorithms, FCAN-MOPSO achieves a better accuracy performance with improved convergence. Hence, FCAN-MOPSO is a promising graph clustering algorithm to precisely and efficiently discover clusters in complex networks.