NDLI: I/O-efficient techniques for computing pagerank

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I/O-efficient techniques for computing pagerank

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I/O-efficient techniques for computing pagerank

Content Provider	ACM Digital Library
Author	Chen, Yen-Yu Suel, Torsten Gan, Qingqing
Abstract	Over the last few years, most major search engines have integrated link-based ranking techniques in order to provide more accurate search results. One widely known approach is the Pagerank technique, which forms the basis of the Google ranking scheme, and which assigns a global importance measure to each page based on the importance of other pages pointing to it. The main advantage of the Pagerank measure is that it is independent of the query posed by a user; this means that it can be precomputed and then used to optimize the layout of the inverted index structure accordingly. However, computing the Pagerank measure requires implementing an iterative process on a massive graph corresponding to billions of web pages and hyperlinks.In this paper, we study I/O-efficient techniques to perform this iterative computation. We derive two algorithms for Pagerank based on techniques proposed for out-of-core graph algorithms, and compare them to two existing algorithms proposed by Haveliwala. We also consider the implementation of a recently proposed topic-sensitive version of Pagerank. Our experimental results show that for very large data sets, significant improvements over previous results can be achieved on machines with moderate amounts of memory. On the other hand, at most minor improvements are possible on data sets that are only moderately larger than memory, which is the case in many practical scenarios.
Starting Page	549
Ending Page	557
Page Count	9
File Format	PDF
ISBN	1581134924
DOI	10.1145/584792.584882
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2002-11-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Link-based ranking Out-of-core Search engines External memory algorithms Pagerank
Content Type	Text
Resource Type	Article

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