  SimRank is a popular measurement for evaluating the node-to-node similarities
based on the graph topology. In recent years, single-source and top-$k$ SimRank
queries have received increasing attention due to their applications in web
mining, social network analysis, and spam detection. However, a fundamental
obstacle in studying SimRank has been the lack of ground truths. The only exact
algorithm, Power Method, is computationally infeasible on graphs with more than
$10^6$ nodes. Consequently, no existing work has evaluated the actual
trade-offs between query time and accuracy on large real-world graphs. In this
paper, we present ExactSim, the first algorithm that computes the exact
single-source and top-$k$ SimRank results on large graphs. With high
probability, this algorithm produces ground truths with a rigorous theoretical
guarantee. We conduct extensive experiments on real-world datasets to
demonstrate the efficiency of ExactSim. The results show that ExactSim provides
the ground truth for any single-source SimRank query with a precision up to 7
decimal places within a reasonable query time.
