Extract the network communities based on fuzzy clustering theory

Abstract

Community detection in complex networks is of great importance in analyzing the interaction patterns and group behaviors. However, the traditional method of community division divide each node in the network into a specific community, while may ignore its internal connection. In this paper, a new strategy that selects a fuzzy function and fuzzy threshold (FF-FT) was presented to discover community structure. Edge dense degree coefficient was introduced to calculate fuzzy relation between nodes, and Fast–Warshall algorithm was used to reduce the complexity of FF-FT. Through the theoretical analysis and the comparison of eight current well-known community detection algorithms on seven real networks and artificial networks with different parameters, the results show that the FF-FT algorithm has a good community detection performance.