Abstract
SimRank is a well-known algorithm for similarity calculation based on object-to-object relationship. However, it suffers from high computation cost. In this paper, we find that the convergence behavior of different object pairs is different when we use SimRank to compute the similarity of objects. Many similarity scores converge fast, while others need more time before convergence. Based on this observation, we propose an adaptive method called Adaptive-SimRank to speed up similarity calculation. Using this method, we don’t need to recalculate those converged pairs’ similarity. The experiments conducted on web datasets and synthetic dataset show that our new method can reduce the running time by nearly 35%.
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© 2009 Springer-Verlag Berlin Heidelberg
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Cai, Y., Liu, H., He, J., Du, X., Jia, X. (2009). An Adaptive Method for the Efficient Similarity Calculation. In: Zhou, X., Yokota, H., Deng, K., Liu, Q. (eds) Database Systems for Advanced Applications. DASFAA 2009. Lecture Notes in Computer Science, vol 5463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00887-0_31
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DOI: https://doi.org/10.1007/978-3-642-00887-0_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00886-3
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