Aiming at the current problems of community detection algorithm in which user’s property is not used; the community structure is not stable and the efficiency of the algorithm is low, this paper proposes a community detection algorithm based on the user influence and its parallelization method. In terms of the concept of user influence in the subject communication and the PageRank algorithm, this paper uses the properties of nodes of users in social networks to form the user influence factors. Then, the user with the biggest influence is set as the initial node of new community and and the local modularity is introduced into detecting the community structure. in order to make the result of community detection quick and efficient. Many experiments show that the improved algorithm can efficiently detect the community structure with large scale users and the results are stable. Therefore, this algorithm will have a wide applied prospect.
social networks; community detection; user influence; PageRank algorithm; local modularity