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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References
Bekas, C., Kokiopoulou, E., Saad, Y.: An estimator for the diagonal of a matrix. Appl. Numer. Math. 57(11–12), 1214–1229 (2007)
Boley, D.L.: Krylov space methods on state-space control models. Circuits Syst. Signal Process. 13, 733–758 (1994)
Fouss, F., Pirotte, A., Renders, J.M., Saerens, M.: Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowl. Data Eng. 19(3), 355–369 (2007)
Fujiwara, Y., Nakatsuji, M., Shiokawa, H., Onizuka, M.: Efficient search algorithm for SimRank. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp. 589–600. IEEE (2013)
Jeh, G., Widom, J.: SimRank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538–543 (2002)
Jeh, G., Widom, J.: Scaling personalized web search. In: Proceedings of the 12th International Conference on World Wide Web, pp. 271–279 (2003)
Kusumoto, M., Maehara, T., Kawarabayashi, K.i.: Scalable similarity search for SimRank. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 325–336 (2014)
Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)
Lu, J., Gong, Z., Yang, Y.: A matrix sampling approach for efficient SimRank computation. Inf. Sci. 556, 1–26 (2021)
Rothe, S., Schütze, H.: CoSimRank: a flexible & efficient graph-theoretic similarity measure. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 1392–1402 (2014)
Saad, Y.: Analysis of some Krylov subspace approximations to the matrix exponential operator. SIAM J. Numer. Anal. 29(1), 209–228 (1992)
Tian, B., Xiao, X.: SLING: a near-optimal index structure for SimRank. In: Proceedings of the 2016 International Conference on Management of Data, pp. 1859–1874 (2016)
Wang, H., Wei, Z., Yuan, Y., Du, X., Wen, J.R.: Exact single-source SimRank computation on large graphs. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 653–663 (2020)
Yu, W., Iranmanesh, S., Haldar, A., Zhang, M., Ferhatosmanoglu, H.: Rolesim*: scaling axiomatic role-based similarity ranking on large graphs. World Wide Web 25(2), 785–829 (2022). https://doi.org/10.1007/s11280-021-00925-z
Yu, W., Lin, X., Zhang, W., Pei, J., McCann, J.A.: Simrank*: effective and scalable pairwise similarity search based on graph topology. VLDB J. 28(3), 401–426 (2019)
Yu, W., McCann, J.A.: Efficient partial-pairs SimRank search on large networks. Proc. VLDB Endow. 8(5), 569–580 (2015)
Yu, W., McCann, J.A., Zhang, C., Ferhatosmanoglu, H.: Scaling high-quality pairwise link-based similarity retrieval on billion-edge graphs. ACM Trans. Inf. Syst. 40(4), 78:1–78:45 (2022). https://doi.org/10.1145/3495209
Yu, W., McCann, J.A.: High quality graph-based similarity search. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 83–92 (2015)
Yu, W., Wang, F.: Fast exact CoSimRank search on evolving and static graphs. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, Lyon, France, 23–27 April 2018, pp. 599–608. ACM (2018). https://doi.org/10.1145/3178876.3186126
Yu, W., Yang, J., Zhang, M., Wu, D.: CoSimHeat: an effective heat kernel similarity measure based on billion-scale network topology. In: WWW 2022: The ACM Web Conference 2022, Virtual Event, Lyon, France, 25–29 April 2022, pp. 234–245. ACM (2022). https://doi.org/10.1145/3485447.3511952
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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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