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
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Recent Advance in Content-based Image Retrieval: A Literature Survey
Wengang Zhou,Houqiang Li,Qi Tian +2 more
TL;DR: Categorize and evaluate those algorithms proposed during the period of 2003 to 2016 for content-based image retrieval and conclude with several promising directions for future research.
Proceedings ArticleDOI
Fast computation of SimRank for static and dynamic information networks
TL;DR: A family of novel approximate SimRank computation algorithms for static and dynamic information networks are developed and their corresponding theoretical justification and analysis are given.
Journal ArticleDOI
Bookmark-coloring algorithm for personalized PageRank computing
TL;DR: A novel bookmark-coloring algorithm (BCA) that computes authority weights over the web pages utilizing the web hyperlink structure and the computed vector is similar to the PageRank vector defined for a page-specific teleportation.
Journal ArticleDOI
The Encyclopedia of Life v2: Providing Global Access to Knowledge About Life on Earth
Cynthia Parr,Nathan R. Wilson,Patrick Leary,Katja Schulz,Kristen D. Lans,Lisa Walley,Jennifer Hammock,Anthony Goddard,Jeremy Rice,Marie Studer,Jeffrey T. G. Holmes,Robert J. Corrigan +11 more
TL;DR: It is shown that it is possible to successfully integrate large amounts of descriptive biodiversity data from diverse sources into a robust, standards-based, dynamic, and scalable infrastructure.
Proceedings ArticleDOI
Topical TrustRank: using topicality to combat web spam
TL;DR: This work proposes the use of topical information to partition the seed set and calculate trust scores for each topic separately and shows that the Topical TrustRank has a better performance than TrustRank in demoting spam sites or pages.
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.