Conference, International Asia-Pacific Web (2010)
Apr. 6, 2010 to Apr. 8, 2010
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/APWeb.2010.47
SimRank is a well known algorithm which conducts link analysis to measure similarity between each pair of nodes (nodepair). But it suffers from high computational cost, limiting its usage in large-scale datasets. Moreover, Links between nodes are changing over time. It may be desirable to quickly approximate the similarity score between certain nodepair without performing a large-scale computation on the entire graph. In our approach we propose a method to efficiently estimate the similarity score using only a small subgraph of the entire graph. We call this novel algorithm “Local-SimRank”. The experimental results conducted on real datasets and synthetic dataset show that our algorithm efficiently produces good approximations to the global SimRank scores. Meanwhile, we prove that the Local-SimRank score LS(a, b) is always less than original SimRank score S(a, b) mathematically.
X. Jia, L. Zou, J. He, X. Du, Y. Cai and H. Liu, "Local Methods for Estimating SimRank Score," Conference, International Asia-Pacific Web(APWEB), Buscan, Korea, 2010, pp. 157-163.