A Method of Computing PageRank Based on Transition Matrix Decomposition

This paper proposes 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 demand is 585MB during the PageRank computation of 17 million Web Pages containing 280 million links,indicating that this method meets the demand for engineering applications.