Open AccessProceedings Article
The PageRank Citation Ranking : Bringing Order to the Web
Lawrence Page,Sergey Brin,Rajeev Motwani,Terry Winograd +3 more
- Vol. 98, pp 161-172
TLDR
This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them, and shows how to efficiently compute PageRank for large numbers of pages.Abstract:
The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.read more
Citations
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Proceedings ArticleDOI
Incremental page rank computation on evolving graphs
TL;DR: This paper exploits the underlying principle of first order markov model on which PageRank is based, to incrementally compute PageRank for the evolving Web graph, and shows significant speed up in computational cost.
Posted Content
Deep Group-shuffling Random Walk for Person Re-identification
TL;DR: Zhang et al. as mentioned in this paper proposed a group-shuffling random walk network for fully utilizing the affinity information between gallery images in both the training and testing processes, which can be integrated into deep neural networks.
Proceedings ArticleDOI
Boosting spectrum-based fault localization using PageRank
TL;DR: PRFL is presented, a lightweight technique that boosts spectrum-based fault localization by differentiating tests using PageRank algorithm, and has been demonstrated to outperform state-of-the-art SBFL techniques significantly.
Journal ArticleDOI
A Reordering for the PageRank Problem
Amy N. Langville,Carl D. Meyer +1 more
TL;DR: Results of an experimental comparison are presented, which demonstrate that the reordered PageRank algorithm can provide a speedup of as much as a factor of 6.
Proceedings ArticleDOI
Socialtrust: tamper-resilient trust establishment in online communities
TL;DR: The SocialTrust framework for tamper-resilient trust establishment in online communities provides community users with dynamic trust values by distinguishing relationship quality from trust; incorporating a personalized feedback mechanism for adapting as the community evolves; and tracking user behavior.
References
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Journal Article
The Anatomy of a Large-Scale Hypertextual Web Search Engine.
Sergey Brin,Lawrence Page +1 more
TL;DR: Google as discussed by the authors is a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.
Journal ArticleDOI
Efficient crawling through URL ordering
TL;DR: In this paper, the authors study in what order a crawler should visit the URLs it has seen, in order to obtain more "important" pages first, and they show that a good ordering scheme can obtain important pages significantly faster than one without.
Proceedings ArticleDOI
Silk from a sow's ear: extracting usable structures from the Web
TL;DR: This paper presents the exploration into techniques that utilize both the topology and textual similarity between items as well as usage data collected by servers and page meta-information lke title and size.
Proceedings ArticleDOI
HyPursuit: a hierarchical network search engine that exploits content-link hypertext clustering
TL;DR: Experience with HyPursuit suggests that abstraction functions based on hypertext clustering can be used to construct meaningful and scalable cluster hierarchies, and is encouraged by preliminary results on clustering based on both document contents and hyperlink structures.
Journal ArticleDOI
The quest for correct information on the Web: hyper search engines
TL;DR: This paper presents a novel method to extract from a web object its “hyper” informative content, in contrast with current search engines, which only deal with the “textual’ informative content.