An Efficient Detecting Communities Algorithm with Self-Adapted Fuzzy C-Means Clustering in Complex Networks

Community structure is an important characteristic in real complex networks, meaning the networks are divided naturally into modules or communities, groups of vertices with relatively dense connections within groups but sparser connections between them. Fuzzy c-means clustering have been proposed to detect the community structure in networks for the past few years, and gained some fruitful results. In this paper, we present a new method to find the community structure in complex networks with self-adapted fuzzy c-means clustering, which can find the sum of the community structure voluntarily and overcome the deficiencies of original Fuzzy c-means clustering algorithms. The simulation results on the real-world networks and on synthetic benchmarks verify that the algorithm is more complete and accurate than the other mainstream FCM algorithms.