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05-06-2021 | Regular Paper | Issue 6/2021

The VLDB Journal 6/2021

ExactSim: benchmarking single-source SimRank algorithms with high-precision ground truths

Journal:
The VLDB Journal > Issue 6/2021
Authors:
Hanzhi Wang, Zhewei Wei, Yu Liu, Ye Yuan, Xiaoyong Du, Ji-Rong Wen
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Abstract

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 accuracy of various single-source and top-k SimRank algorithms 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. This algorithm produces ground truths with precision up to 7 decimal places with high probability. With the ground truths computed by ExactSim, we present the first experimental study of the accuracy/cost trade-offs of existing approximate SimRank algorithms on large real-world graphs and synthetic graphs. Finally, we use the ground truths to exploit various properties of SimRank distributions on large graphs.

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