Fast and Exact Top-k Algorithm for PageRank

In AI and Web communities, many applications utilize PageRank. To obtain high PageRank score nodes, the original approach iteratively computes the PageRank score of each node until convergence by using the whole graph. If the graph is large, this approach is infeasible due to its high computational cost. The goal of this study is to find top-k PageRank score nodes efficiently for a given graph without sacrificing accuracy. Our solution, F-Rank, is based on two ideas: (1) It iteratively estimates lower/upper bounds of PageRank scores, and (2) It constructs subgraphs in each iteration by pruning unnecessary nodes and edges to identify top-k nodes. Our theoretical analysis shows that F-Rank guarantees result exactness. Experiments show that F-Rank finds top-k nodes much faster than the original approach.