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论文类型：会议论文

发表时间：2008-10-12

收录刊物：EI、CPCI-S、Scopus

页面范围：11500-+

关键字：Google; PageRank; BA model; growth; preferential attachment

摘要：While considering the preferential attachment mechanism of networks, the classical BA scale-free network model sets the degrees of nodes in networks as the prime factor to decide the probability of the preferential attachment, whereas the affection of other important information of the structure of networks was ignored. The excellent performance of the Google search engine shows that the PageRank, which is defined in the PageRank algorithm, can describe the importance of the nodes in networks better than the degree. In this paper, based on the PageRank algorithm of the Google search engine, we build a new model of growing networks by setting the PageRanks of nodes as the measurement of the probability of preferential attachment, and analyze its prime characters. The results of numerical simulation show that the new model could reflect some important characters of actual networks well.