Abstract
SimRank is a popular node-pair similarity search model based on graph topology. It has received sustained attention due to its wide range of applications in real-world scenarios. Considerable effort has been devoted to devising fast algorithms for SimRank computation through either iterative approaches or random walk based methods. In this paper, we propose an efficient accuracy-aware algorithm for computing single-source SimRank similarity. First, we devise an algorithm, ApproxDiag, to approximate the diagonal correction matrix. Next, we propose an efficient algorithm, named SimSky, which utilizes two Krylov subspaces for transforming high-dimensional single-source SimRank search into low-dimensional matrix-vector multiplications. Extensive experiments on various real datasets demonstrate the superior search quality of SimSky compared to other competitors.
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Notes
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d denotes the average node degree.
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This work has been supported by the National Natural Science Foundation of China under Grant No. 61972203.
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We acknowledge the importance of ethical considerations in the design of our ApproxDiag and SimSky algorithms. All the datasets used in this paper are publicly-available online, and do not have any privacy issues. We ensure that our algorithms do not lead to any potential negative influences. We declare that we allow our algorithms to be used for the benefit of society.
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Yan, L., Yu, W. (2023). SimSky: An Accuracy-Aware Algorithm for Single-Source SimRank Search. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14171. Springer, Cham. https://doi.org/10.1007/978-3-031-43418-1_14
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