Gauging correct relative ranking on similarity search


One of the important tasks in link analysis is to quantify the similarity between two objects based on hyperlink structure. SimRank is an attractive similarity measure of this type. Existing work mainly focuses on absolute SimRank scores, and often harnesses an iterative paradigm to compute them. While these iterative scores converge to exact ones with the increasing number of iterations, it is still notoriously difficult to determine how well the relative orders of these iterative scores can be preserved for a given iteration. In this paper, we propose efficient ranking criteria that can secure correct relative orders of node-pairs with respect to SimRank scores when they are computed in an iterative fashion. Moreover, we show the superiority of our criteria in harvesting top-K SimRank scores and bucket orders from a full ranking list. Finally, viable empirical studies verify the usefulness of our techniques for SimRank top-K ranking and bucket ordering.

Publication DOI:
Divisions: Engineering & Applied Sciences
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Event Title: 24th ACM International on Conference on Information and Knowledge Management
Event Type: Other
Event Dates: 2015-10-18 - 2015-10-23
ISBN: 978-1-4503-3794-6
PURE Output Type: Conference contribution
Published Date: 2015-10-17
Authors: Yu, Weiren
McCann, Julie



Version: Accepted Version

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