On Multivariate Markov Chains for Common and Non-Common Objects in Multiple Networks  

作　　者：Xutao Li Wen Li Michael K.Ng Yunming Ye 

机构地区：[1]Department of Computer Science,Shenzhen Graduate School,Harbin Institute of Technology,Shenzhen,518055,China [2]School of Mathematical Sciences,South China Normal University,Guangzhou,510631,China [3]Department of Mathematics,Hong Kong Baptist University,Kowloon Tong,Hong Kong

出　　处：《Numerical Mathematics(Theory,Methods and Applications)》2012年第3期384-402,共19页高等学校计算数学学报（英文版）

基　　金：supported in part by National Natural Science Foundations of China(Nos.10671077,10971075);Research Fund for the Doctoral Program of Higher Education of China(No.20104407110002);Guangdong Provincial Natural Science Foundations,P.R.China(No.9151063101000021);supported in part by NSFC under Grant no.61073195,Shenzhen Science and Technology Program under Grant no.CXB201005250024A and ZD201006100018A;Natural Scientific Research Innovation Foundation in HIT under Grant no.HIT.NSFIR.2010128.

摘　　要：Node importance or centrality evaluation is an important methodology for network analysis.In this paper,we are interested in the study of objects appearing in several networks.Such common objects are important in network-network interactions via object-object interactions.The main contribution of this paper is to model multiple networks where there are some common objects in a multivariate Markov chain framework,and to develop a method for solving common and non-common objects’stationary probability distributions in the networks.The stationary probability distributions can be used to evaluate the importance of common and non-common objects via network-network interactions.Our experimental results based on examples of co-authorship of researchers in different conferences and paper citations in different categories have shown that the proposed model can provide useful information for researcher-researcher interactions in networks of different conferences and for paperpaper interactions in networks of different categories.

关 键 词：transition probability multivariate Markov chains stationary probability distribution IRREDUCIBLE multiple networks 

分 类 号：TN9[电子电信—信息与通信工程]