基于逆向游走的PageRank社交网络影响力度量算法

A social networks influence measurement algorithm based on reverse walk PageRank

随着社交网络的发展,其节点影响力度量成为一个重要的研究领域。针对传统随机游走PageRank算法精确度不高的问题,提出一种逆向随机游走PageRank算法,该算法采用逆向查找消息传播源的思想,对网络中的每条有向边以概率ε进行逆向随机游走,通过迭代计算出每个节点的PageRank值。实验表明,本文提出的算法较传统的随机游走PageRank算法具有更好的稳定性,并在迭代次数较少时也能保持较高精度。

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 al- gorithm, 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 ,and the value of PageRank is calculated by iteration. Experimental evaluation on publicly availa- ble datasets demonstrates that our algorithm has the improved stability and higher accuracy when there is less iteration.