Abstract: To address the issue that the traditional community detection algorithm is difficult to apply to large-scale complex heterogeneous mobile networks, a mobile network model is constructed using mobile network usage detail record （UDR） and users’ social relationship data, and a method for mobile community detection based on multi-dimensional informational fusion is proposed, called BNMF-NF. Firstly, the paper comprehensively considers the user’s social relationship and spatiotemporal behavior, and gives the user’s social similarity, spatiotemporal distribution similarity and topic preference similarity. Then, the weighted network fusion method is used to fuse multi-dimensional similarity relations to construct a user similarity network. Finally, the community structure of the mobile network is detected by the use of the bounded non-negative matrix factorization. Experimental results on Foursquare and telecom data sets show that the method can effectively detect the community structure in the mobile network.

Key words: community detection, mobile network, user similarity, similarity network fusion, non-negative matrix factorization