Community Discovery Technology In Social Networks

Development of Internet technology is driving people into the information society,people live in a world with a variety of complex networks in the world.Social networks as one of the new,practical dating mode,dependent on its authenticity,stability and other characteristics of people of all ages have been playing an increasingly important role in the network activity.Because of interpersonal,social networks tend to have a significant community structure characteristics.People in the same community have frequent contact,less contact in different community.Through the study of the social networks of community we can find more information in complex network topologies,discover its hidden information,predict future network trends and so on.In many respects,community detection has important practical significance and role.In this paper we present a new complex network community detection algorithm research using membership function of fuzzy mathematics.So that nodes attribution can be divided into clusters based on the value of membership function.In the analysis process,both artificial data and real data of social networks are used to compare with other algorithms.When using real data,distributed capture and storage method are used to improve the efficiency and reliability of processing.The experiments show that the algorithm is effective and reliable in time complexity and robustness,and it is possible to extract the true clustering information in a heterogeneous network.In a real social networks we can mine the hiding information under multidimensional networks.