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.
胡开先，梁英，苏立新，许洪波，傅川. 基于完全子图的社交网络用户特征识别方法*[J]. 模式识别与人工智能, 2016, 29(8): 698-708.
HU Kaixian, LIANG Ying, SU Lixin, XU Hongbo, FU Chuan. Method for Social Network User Feature Recognition Based on Clique. , 2016, 29(8): 698-708.
[1] GONZLEZ M C, HIDALGO C A, BARABSI A L. Understan-ding Individual Human Mobility Patterns. Nature, 2008, 453(7196): 779-782. [2] DAVIS JR C A, PAPPA G L, DE OLIVEIRA D R R, et al. Inferring the Location of Twitter Messages Based on User Relationships. Transactions in GIS, 2011, 15(6): 735-751. [3] BACKSTROM L, SUN E, MARLOW C. Find Me if You Can: Improving Geographical Prediction with Social and Spatial Proximity // Proc of the 19th International Conference on World Wide Web. New York, USA: ACM, 2010: 61-70. [4] XU W H, ZHOU X, LI L. Inferring Privacy Information via Social Relations // Proc of the 24th International Conference on Data Engineering Workshop. New York, USA: IEEE, 2008: 525-530. [5] WU W, ZHANG B, OSTENDORF M. Automatic Generation of Personalized Annotation Tags for Twitter Users // Proc of the Annual Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg, USA: Association for Computational Linguistics, 2010: 689-692. [6] TAN B, L Y H, ZHAI C X. Mining Long-Lasting Exploratory User Interests from Search History // Proc of the 21st ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2012: 1477-1481. [7] BAO J, ZHENG Y, MOKBEL M F. Location-Based and Preference-Aware Recommendation Using Sparse Geo-Social Networking Data // Proc of the 20th International Conference on Advances in Geographic Information Systems. New York, USA: ACM, 2012: 199-208. [8] LIU B, XIONG H. Point-of-Interest Recommendation in Location Based Social Networks with Topic and Location Awareness // Proc of the SIAM International Conference on Data Mining. Philadelphia, USA: SIAM, 2013, 13: 396-404. [9] WEI L Y, YEH M Y, LIN G, et al. Discovering Point-of-Interest Signatures Based on Group Features from Geo-Social Networking Data // Proc of the Conference on Technologies and Applications of Artificial Intelligence. New York, USA: IEEE, 2013: 182-187. [10] CRANDALL D J, BACKSTROM L, COSLEY D, et al. Inferring Social Ties from Geographic Coincidences. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107(52): 22436-22441. [11] 王 玙,高 琳. 基于社交圈的在线社交网络朋友推荐算法.计算机学报, 2014, 37(4): 801-808. (WANG Y, GAO L. Social Circle-Based Algorithm for Friend Recommendation in Online Social Networks. Chinese Journal of Computers, 2014, 37(4): 801-808.) [12] YOSHIDA T. Toward Finding Hidden Communities Based on User Profiles. Journal of Intelligent Information Systems, 2013, 40(2): 189-209. [13] CHEN J, SAAD Y. Dense Subgraph Extraction with Application to Community Detection. IEEE Trans on Knowledge and Data Engin-
eering, 2010, 24(7): 1216-1230. [14] YIN Z J, GUPTA M, WENINGER T, et al. LINKREC: A Unified Framework for Link Recommendation with User Attributes and Graph Structure // Proc of the 19th International Conference on World Wide Web. New York, USA: ACM, 2010: 1211-1212. [15] NARAYANAN A, SHMATIKOV V. De-anonymizing Social Networks // Proc of the 30th IEEE Symposium on Security and Privacy. Washington, USA: IEEE, 2009: 173-187. [16] NARAYANAN A, PASKOV H, GONG N Z Q, et al. On the Feasibility of Internet-Scale Author Identification // Proc of the IEEE Symposium on Security and Privacy. Washington, USA: IEEE, 2012: 300-314. [17] CHRISTAKIS N A, FOWLER J H. Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives - How Your Friends' Friends' Friends Affect Everything You Feel, Think, and Do. New York, USA: Back Bay Books, 2011. [18] LAZARSFELD P F, MERTON R K. Friendship as a Social Process: A Substantive and Methodological Analysis // Dahl R A, Berger M, Abel T, et al., eds. Freedom and Control in Modern Society. Buckhannon, USA: Van Nostrand, 1954: 18-66. [19] MCPHERSON M, SMITH-LOVIN L, COOK J M. Birds of a Feather: Homophily in Social Networks. Annual Review of Socio-logy, 2001, 27(1): 415-444. [20] SHALIZI C R, THOMAS A C. Homophily and Contagion are Generically Confounded in Observational Social Network Studies. Sociological Methods and Research, 2011, 40(2): 211-239. [21] 胡开先.社交网络用户身份特征识别的虚实映射方法研究.硕士学位论文.北京:中国科学院大学, 2015. (HU K X. Study of Mapping Method for Social Network User Identity Feature Recognition.Maser Dissertation. Beijing, China: University of Chinese Academy of Sciences, 2015)
2016, Vol. 29 
