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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Graph Kernels
TL;DR: A unified framework to study graph kernels is presented and a kernel that is close to the optimal assignment kernel of kernel of Frohlich et al. (2006) yet provably positive semi-definite is provided.
Proceedings ArticleDOI
GraphX: graph processing in a distributed dataflow framework
TL;DR: This paper introduces GraphX, an embedded graph processing framework built on top of Apache Spark, a widely used distributed dataflow system and demonstrates that GraphX achieves an order of magnitude performance gain over the base dataflow framework and matches the performance of specialized graph processing systems while enabling a wider range of computation.
Proceedings ArticleDOI
Local Graph Partitioning using PageRank Vectors
TL;DR: An improved algorithm for computing approximate PageRank vectors, which allows us to find a cut with conductance at most oslash and approximately optimal balance in time O(m log4 m/oslash) in time proportional to its size.
Proceedings ArticleDOI
HT06, tagging paper, taxonomy, Flickr, academic article, to read
TL;DR: A model of tagging systems, specifically in the context of web-based systems, is offered to help illustrate the possible benefits of these tools and a simple taxonomy of incentives and contribution models is provided to inform potential evaluative frameworks.
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.
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.