MARKOV CHAIN-BASED ANALYSIS OF THE DEGREE DISTRIBUTION FOR A GROWING NETWORK  

作　　者：候振挺 童金英 史定华 

机构地区：[1]School of Mathematical Science and Computing Technology, Central South University [2]School of Sciences, Donghua University [3]Department of Mathematics, Shanghai University

出　　处：《Acta Mathematica Scientia》2011年第1期221-228,共8页数学物理学报（B辑英文版）

基　　金：supported by the National Natural Science Foundation (11071258, 60874083, 10872119, 10901164)

摘　　要：In this article, we focus on discussing the degree distribution of the DMS model from the perspective of probability. On the basis of the concept and technique of first-passage probability in Markov theory, we provide a rigorous proof for existence of the steady-state degree distribution, mathematically re-deriving the exact formula of the distribution. The approach based on Markov chain theory is universal and performs well in a large class of growing networks.In this article, we focus on discussing the degree distribution of the DMS model from the perspective of probability. On the basis of the concept and technique of first-passage probability in Markov theory, we provide a rigorous proof for existence of the steady-state degree distribution, mathematically re-deriving the exact formula of the distribution. The approach based on Markov chain theory is universal and performs well in a large class of growing networks.

关 键 词：Growing networks preferential attachment power law 

分 类 号：O211.62[理学—概率论与数理统计]