Abstract
SimRank is an attractive structural-context measure of similarity between two objects in a graph. It recursively follows the intuition that "two objects are similar if they are referenced by similar objects". The best known matrix-based method [1] for calculating SimRank, however, implies an assumption that the graph is non-singular, its adjacency matrix is invertible. In reality, non-singular graphs are very rare; such an assumption in [1] is too restrictive in practice. In this paper, we provide a treatment of [1], by supporting similarity assessment on non-invertible adjacency matrices. Assume that a singular graph G has n nodes, with r(2+Kr2)) time for K iterations. In contrast, the only known matrix-based algorithm that supports singular graphs [1] needs O(r4n2) time. The experimental results on real and synthetic datasets demonstrate the superiority of InvSR on singular graphs against its baselines.
Original language | English |
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Title of host publication | SIGIR '14 Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval |
Place of Publication | New York, NY (US) |
Publisher | ACM |
Pages | 859-862 |
Number of pages | 4 |
ISBN (Print) | 978-1-4503-2257-7 |
DOIs | |
Publication status | Published - 3 Jul 2014 |
Event | 37th international ACM SIGIR conference on Research & Development in Information Retrieval - Gold Coast, Queensland, Australia Duration: 6 Jul 2014 → 11 Jul 2014 |
Conference
Conference | 37th international ACM SIGIR conference on Research & Development in Information Retrieval |
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Abbreviated title | SIGIR 2014 |
Country/Territory | Australia |
City | Gold Coast, Queensland |
Period | 6/07/14 → 11/07/14 |