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

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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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Zhewei Wei was supported by National Natural Science Foundation of China (NSFC) No. 61972401 and No. 61932001, by the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China under Grant 18XNLG21, and by Alibaba Group through Alibaba Innovative Research Program. The work is partially done at Beijing Key Laboratory of Big Data Management and Analysis Methods, MOE Key Lab DEKE, Renmin University of China, and Pazhou Lab, Guangzhou, 510330, China. Hanzhi Wang was supported by the Outstanding Innovative Talents Cultivation Funded Programs 2020 of Renmin University of China. Ye Yuan was supported by NSFC No. 61932004 and No. 61622202 and by FRFCU No. N181605012. Ji-Rong Wen was supported by NSFC No. 61832017 and by Beijing Outstanding Young Scientist Program NO. BJJWZYJH012019100020098. Xiaoyong Du was supported by NSFC No. U1711261.

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Wang, H., Wei, Z., Liu, Y. et al. ExactSim: benchmarking single-source SimRank algorithms with high-precision ground truths. The VLDB Journal 30, 989–1015 (2021).

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