【作者】 刘松彬； 都云程； 施水才；

【Author】 Liu Songbin Du Yuncheng Shi Shuicai (Chinese Information Processing Research Center,Beijing Information Science & Technology University,Beijing 100101)

【机构】 北京信息科技大学中文信息处理研究中心；

【摘要】 提出了一种基于分解转移矩阵的 PageRank 的迭代计算方法。该方法对 PageRank 理论模型进一步推导, 把其 Markov 状态转移矩阵进行了分解,从而降低存储开销和计算复杂度,减少 I/O 需求,使得 PageRank 计算的工程化实现更为简单。实验表明1700多万的网页28亿条链接,可以在30秒内完成一次迭代,内存需求峰值 585MB,可以满足工程化应用的需求。更多还原

【Abstract】 We have proposed a method of computing PageRank based on transfer matrix decomposition.Based on the PageRank random surfer model,the method decomposes the Markov states transfer matrix,so that the memory cost, computational complexity and I/O needs are reduced drastically.Experiments show that each iteration can be completed in 30 seconds and that the peak memory demands is 585MB during the computation of 17 million Web Pages containing 280 million links,indicating that this method meets the demand for engineering applications.更多还原