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
More filters
Journal ArticleDOI
Are people biased in their use of search engines
TL;DR: Assessing user search behavior when deciding which links to follow in rank-ordered results lists shows users are more likely to click on links that lead to other links in the list.
Proceedings ArticleDOI
Automatic video tagging using content redundancy
TL;DR: This paper reveals redundancy in YouTube videos using robust content-based video analysis techniques and proposes different tag propagation methods for automatically obtaining richer video annotations that lead to enhanced feature representations for applications such as automatic data organization and search.
Research Article Cognitive Foundations for Science Assessment Design: Knowing What Students Know About Evolution
TL;DR: This article applied the assessment triangle to design and evaluate new items for an instrument (ACORNS, Assessing Contextual Reasoning about Natural Selection) that had been proposed to assess students' use of natural selection to explain evolutionary change.
Journal ArticleDOI
GraphIt: a high-performance graph DSL
TL;DR: GraphIt is introduced, a new DSL for graph computations that generates fast implementations for algorithms with different performance characteristics running on graphs with different sizes and structures and which outperforms the next fastest shared-memory frameworks on 24 out of 32 experiments.
Book ChapterDOI
Neural Networks for Fast Estimation of Social Network Centrality Measures
TL;DR: It is shown that neural networks can be effective in learning and estimating the ordering of vertices in a social network based on centrality measures, requiring far less computational effort, and proving to be faster than early termination of the power grid method that can be used for computing these measures.
References
More filters
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.