Method for Social Network User Feature Recognition Based on Clique
HU Kaixian1,2, LIANG Ying1, SU Lixin1,2, XU Hongbo1, FU Chuan1
1.Key Laboratory of Web Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190 2.University of Chinese Academy of Sciences, Beijing 100049
Abstract Social network is a major media for people to get different information and make friends. As the social network keeps developing, it brings convenience to people but meanwhile identifying user identity becomes difficult. To solve this problem, a method for social network user feature recognition based on clique is proposed. According to three degrees of influence rule, the inference model is built, and through the analysis of the clique consisting of user attributes in the social network topology structure, the unknown identity of the current user is inferred. Identity feature recognition methods based on clique are put forward. They are the current user included clique identity recognition method and the multi-degree passing clique identity recognition method. In both methods,the adjacent matrix of social network topology graph of current three-degree friends of user is used to infer the unknown identity of current user via major voting scheme. By the proposed method, the problem of unstable user feature recognition caused by the lack of social relationship is effectively solved. The experimental result shows the good precision of the proposed method.
Fund:Supported by National Basic Research Program of China (973 Program) (No.2014CB340406, 2012CB316303, 2013CB329602), National High Technology Research and Development Program of China (863 Program) (No.2015AA015803), State Key Program of National Natural Science Foundation of China (No.61232010), General Program of National Natural Science Foundation of China(No.61173064), National Key Technology Research and Development Program of the Ministry of Science and Technology of China (No.2015BAK20B03).
About author:: (HU Kaixian, born in 1989, master. His research interests include network data science and big data.)(LIANG Ying(Corresponding author), born in 1962, master, senior engineer. Her research interests include big data mining, big data processing, middleware and service computing.)(SU Lixin, born in 1992, master student. His research inte-rests include data science and big data.)(XU Hongbo, born in 1975, Ph. D., associate professor. His research interests include web search, big data mining, text classification and information filtering.)(FU Chuan, born in 1973, master, senior engineer. His research interests include big data processing, middleware, ser-vice computing and network architecture.)
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2016, Vol. 29 
