A Survey on PageRank Computing
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The theoretical foundations of the PageRank formulation are examined, the acceleration of PageRank computing, in the effects of particular aspects of web graph structure on the optimal organization of computations, and in PageRank stability.Abstract:
This survey reviews the research related to PageRank computing. Components of a PageRank vector serve as authority weights for web pages independent of their textual content, solely based on the hyperlink structure of the web. PageRank is typically used as a web search ranking component. This defines the importance of the model and the data structures that underly PageRank processing. Computing even a single PageRank is a difficult computational task. Computing many PageRanks is a much more complex challenge. Recently, significant effort has been invested in building sets of personalized PageRank vectors. PageRank is also used in many diverse applications other than ranking. We are interested in the theoretical foundations of the PageRank formulation, in the acceleration of PageRank computing, in the effects of particular aspects of web graph structure on the optimal organization of computations, and in PageRank stability. We also review alternative models that lead to authority indices similar to PageRan...read more
Citations
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Proceedings ArticleDOI
Expertise networks in online communities: structure and algorithms
TL;DR: This study analyzes the Java Forum, a large online help-seeking community, using social network analysis methods and tests a set of network-based ranking algorithms on this large size social network in order to identify users with high expertise.
Journal ArticleDOI
PageRank Beyond the Web
TL;DR: Google's PageRank method was developed to evaluate the importance of web-pages via their link structure and apply to any graph or network in any domain this paper, however, the mathematics of PageRank are entirely general and can be used to evaluate any graph and any network.
BookDOI
Managing and Mining Graph Data
Charu C. Aggarwal,Haixun Wang +1 more
TL;DR: This is the first comprehensive survey book in the emerging topic of graph data processing and contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy.
Journal ArticleDOI
Random walks and diffusion on networks
TL;DR: The theory and applications of random walks on networks are surveyed, restricting ourselves to simple cases of single and non-adaptive random walkers, and three main types are distinguished: discrete-time random walks, node-centric continuous-timerandom walks, and edge-centric Continuous-Time random walks.
Journal ArticleDOI
Random walks and diffusion on networks
TL;DR: Random walks have been studied for many decades on both regular lattices and (especially in the last couple of decades) on networks with a variety of structures as discussed by the authors, and they are one of the most fundamental types of stochastic processes; can be used to model numerous phenomena, including diffusion, interactions, and opinions among humans and animals; and can extract information about important entities or dense groups of entities in networks.
References
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Journal ArticleDOI
The anatomy of a large-scale hypertextual Web search engine
Sergey Brin,Lawrence Page +1 more
TL;DR: This paper provides an in-depth description of Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and looks at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
Proceedings Article
The PageRank Citation Ranking : Bringing Order to the Web
TL;DR: 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.
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
Information Theory and Statistical Mechanics. II
TL;DR: In this article, the authors consider statistical mechanics as a form of statistical inference rather than as a physical theory, and show that the usual computational rules, starting with the determination of the partition function, are an immediate consequence of the maximum-entropy principle.