A Global Eigenvalue-Driven Balanced Deconvolution Approach for Network Direct-Coupling Analysis

摘要：

　　It is an important and unsettled issue to distinguish direct dependencies from the indirect ones without any prior knowledge in biological networks and social networks,which contain important biological features and coauthorship information.We present a new algorithm,called balanced network deconvolution (BND),by exploiting eigen-decomposition and the statistical behavior of the eigenvalues of random symmetric matrices.Specially,the BND is a parameter-free algorithm that can be directly applied to different networks.Experimental results establish BND as a robust and general approach for filtering the transitive noise on various input matrices generated by different prediction algorithms.

关键词： Network direct-coupling BND Eigenvalue transformation Transitive noise model

作者: Haiping Sun Hongbin Shen

作者单位: Institute of Image Processing and Pattern Recognition,Shanghai Jiao Tong University,and Key Laboratory of System Control and Information Processing,Ministry of Education of China,Shanghai,200240,China

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 409-418

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）