Mobile network community discovery has been introduced as a new efficient way to disseminate mobile internet services to a particular group of mobi le users.This paper builds a mobile network model according to the actual data of mobile communication and proposes a mobile community discovery algorithm based on users network effects.Firstly,the algorithm converts the effect of mobile users on the network into the relationship between vectors in Euclid space by signaling transmission.And then Euclidean distance is used to calculate users’ similarity.Secondly,community structure of the mobile communication network is detected by use of the efficient affinity propagation clustering and the corresponding core user of each community is marked.Finally,the algorithm is proved effective and its related parameters choice is analyzed in the experiments.