Fast incremental SimRank on link-evolving graphs


SimRank is an arresting measure of node-pair similarity based on hyperlinks. It iteratively follows the concept that 2 nodes are similar if they are referenced by similar nodes. Real graphs are often large, and links constantly evolve with small changes over time. This paper considers fast incremental computations of SimRank on link-evolving graphs. The prior approach [12] to this issue factorizes the graph via a singular value decomposition (SVD) first, and then incrementally maintains this factorization for link updates at the expense of exactness. Consequently, all node-pair similarities are estimated in O(r4n2) time on a graph of n nodes, where r is the target rank of the low-rank approximation, which is not negligibly small in practice. In this paper, we propose a novel fast incremental paradigm. (1) We characterize the SimRank update matrix ΔS, in response to every link update, via a rank-one Sylvester matrix equation. By virtue of this, we devise a fast incremental algorithm computing similarities of n2 node-pairs in O(Kn2) time for K iterations. (2) We also propose an effective pruning technique capturing the “affected areas” of ΔS to skip unnecessary computations, without loss of exactness. This can further accelerate the incremental SimRank computation to O(K(nd+|AFF|)) time, where d is the average in-degree of the old graph, and |AFF| (≤ n2) is the size of “affected areas” in ΔS, and in practice, |AFF| ≪ n2. Our empirical evaluations verify that our algorithm (a) outperforms the best known link-update algorithm [12], and (b) runs much faster than its batch counterpart when link updates are small.

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Divisions: College of Engineering & Physical Sciences
Additional Information: This paper has received CiSRA Best Research Paper Award in 2014. © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Event Title: 2014 IEEE 30th International Conference on Data Engineering
Event Type: Other
Event Dates: 2014-03-31 - 2014-04-04
ISBN: 978-1-4799-2555-1
Last Modified: 04 Mar 2024 08:08
Date Deposited: 24 Jan 2017 16:50
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Related URLs: https://ieeexpl ... ocument/6816660 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2014-05-19
Authors: Yu, Weiren (ORCID Profile 0000-0002-1082-9475)
Lin, Xuemin
Zhang, Wenjie



Version: Accepted Version

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