Incorporating Generalized Momentum Method to Accelerate Clustering Analysis of Complex Networks

Many complicated systems can be represented by complex networks. Their accurate clustering analysis plays a critical role in understanding their intrinsic organizations. An effective Fuzzy-based Clustering Algorithm for Networks (FCAN) has thus been developed. However, its major disadvantage is its slow convergence to optimal or near-optimal solutions. To overcome this problem, we make use of a generalized momentum method to accelerate it and accordingly propose a fast fuzzy clustering algorithm, namely F2 CAN. Experimental results on several practical datasets demonstrate that F2 CAN performed better than FCAN in terms of efficiency while maintaining the same-level accuracy. Hence, it is more promising to conduct an accurate and fast clustering analysis for complex networks.