Community detection algorithm based on local affinity propagation and user profile - Kun GUO; Wen-zhong GUO; Qi-rong QIU; Qi-shan ZHANG (Tongxin xuebao)
An algorithm based on local affinity propagation and a new similarity measure concerning user profile is proposed.On one hand,by loosening the exemplar constraint and requiring the messages propagate around a node's neighbors,the algorithm achieves lower time and space complexity without too much lost in clustering accuracy,which makes it adaptable to the mining of large-scale social networks.On the other hand,by designing a hybrid similarity measure based on the topological similarity and the profile similarity of the nodes,the algorithm can effectively tackle the situation of the social networks data without complete user relation information.The experimental results on the artificial datasets and the real-world datasets demonstrate that the algorithm not only has near-linear time complexity and linear space complexity,but also retains high detecting accuracy when handling incomplete networks.