Partial sums-based P-Rank computation in information networks

Abstract

P-Rank is a simple and captivating link-based similarity measure that extends SimRank by exploiting both in- and out-links for similarity computation. However, the existing work of P-Rank computation is expensive in terms of time and space cost and cannot efficiently support similarity computation in large information networks. For tackling this problem, in this paper, we propose an optimization technique for fast P-Rank computation in information networks by adopting the spiritual of partial sums. We write P-Rank equation based on partial sums and further approximate this equation by setting a threshold for ignoring the small similarity scores during iterative similarity computation. An optimized similarity computation algorithm is developed, which reduces the computation cost by skipping the similarity scores smaller than the give threshold during accumulation operations. And the accuracy loss estimation under the threshold is given through extensive mathematical analysis. Extensive experiments demonstrate the effectiveness and efficiency of our proposed approach through comparing with the straightforward P-Rank computation algorithm.