Abstract:

(School of Mathematics and Computer Science,Anhui Normal University,Wuhu 241003,China)Abstract:With the development of social networks, the influence measurement of nodes has become an important research area. Aiming at the accuracy problem of the traditional random walk PageRank algorithm, we in this paper propose a reverse random walk PageRank algorithm, which is based on the idea of reversely searching dissemination source, each directed edge starts the random walk with probability ε,and the value of PageRank is calculated by iteration. Experimental evaluation on publicly available datasets demonstrates that our algorithm has the improved stability and higher accuracy when there is less iteration.

Key words: PageRank;random walk;social networks;influence measurement