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Initially used to rank web pages, PageRank has now been applied in manyfields. With the growing scale of graph, accelerating PageRank computing isurged and designing parallel algorithm is a feasible solution. In this paper,two parallel PageRank algorithms IFP1 and IFP2 are proposed via improving thestate-of-the-art Personalized PageRank algorithm, i.e., Forward Push.Theoretical analysis indicates that, IFP1 can take advantage of the DAGstructure of the graph, where the dangling vertices improves the convergencerate and the unreferenced vertices decreases the computation amount. As animprovement of IFP1, IFP2 pushes mass to the dangling vertices only once butrather many times, and thus decreases the computation amount further.Experiments on six data sets illustrate that both IFP1 and IFP2 outperformPower method, where IFP2 with 38 parallelism can be at most 50 times as fast asthe Power method.

Two Parallel PageRank Algorithms via Improving Forward Push