大规模社会网络敏感链接推理方法-计算机科学与探索.pdf

王淼，张啸剑，孟小峰.大规模社会网络敏感链接推理方法[J].计算机科学与探索，2013 ，7（4 ）：304-314. ISSN 1673-9418 CODEN JKYTA8 E-mail: fcst@vip. 163.com Journal of Frontiers of Computer Science and Technology 1673-9418/2013/07(04)-0304-11 Tel: +86-10 doi: 10.3778/j.issn. 1673-9418.1208023 大规模社会网络敏感链接推理方法* 1+ 1,2 1 王 淼 ，张啸剑 ，孟小峰 1. 中国人民大学 信息学院，北京 100872 2. 河南财经政法大学 计算机与信息工程学院，郑州 450002 Inferring Sensitive Link in Large-Scale Social Networks WANG Miao1+ , ZHANG Xiaojian1,2 , MENG Xiaofeng1 1. School of Information, Renmin University of China, Beijing 100872, China 2. School of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou 450002, China + Corresponding author: E-mail: wangmiao@ WANG Miao, ZHANG Xiaojian, MENG Xiaofeng. Inferring sensitive link in large-scale social networks. Journal of Frontiers of Computer Science and Technology, 2013, 7(4) ：304-314. Abstract: Many applications of social networks require link anonymity due to the sensitive nature of relationship, while link inference used by attackers in social networks could lead to link disclosure. To disclose the sensitive link relationship between nodes, attackers always depend on graph structure features to carry out their attacking objectives. The previous work only focuses on single structure feature as the background knowledge as well as overlooks the structural proximity of nodes, which will result in inferior inference. Therefore, this paper proposes an efficient inference framework based on node proximity in large-scale social networks. This framework includes matrix decomposition based on graph-cl