Perturbation of the Hyper-Linked Environment

Borodin, Allan

doi:10.1007/3-540-45071-8_29

Cited by 17 publications

(6 citation statements)

References 11 publications

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“…We do not know how it performs on real web graphs. Lee,Golub,and Zenios [Lee et al 03] study the performance on relatively large web graphs of a two-stage decomposition technique related to an aggregation of dangling pages (corresponding to (3.8) with q = 2). In the language of Markov chains, the property of having upper triangular form is called lumpability.…”

Section: Advanced Numerical Linear Algebra Methodsmentioning

confidence: 99%

A Survey on PageRank Computing

Berkhin¹

2005

Internet Mathematics

400

295

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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 PageRank and the role of such indices in applications other than web search. We also discuss linkbased search personalization and outline some aspects of PageRank infrastructure from associated measures of convergence to link preprocessing.

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Section: Advanced Numerical Linear Algebra Methodsmentioning

confidence: 99%

A Survey on PageRank Computing

Berkhin¹

2005

Internet Mathematics

400

295

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“…Following the work of Borodin et al [2001], Lempel and Moran [2003] proved that the HITS and PAGERANK algorithms are not weakly rank stable on the class G AC n of authority connected graphs. Recently, Lee and Borodin [2003] considered a different definition of stability where, given a change ∂ = {G, G }, the distance between the weight vectors of an LAR algorithm on graphs G and G may depend on the weights of the nodes whose in and out links were affected. The intuition is that, if a change is performed on a highly authoritative node, then we expect a large change in the weights.…”

Section: 43mentioning

confidence: 99%

Link analysis ranking: algorithms, theory, and experiments

Borodin

Roberts

Rosenthal

et al. 2005

ACM Trans. Internet Technol.

Self Cite

243

147

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The explosive growth and the widespread accessibility of the Web has led to a surge of research activity in the area of information retrieval on the World Wide Web. The seminal papers of Kleinberg [1998, 1999] and Brin and Page [1998] introduced Link Analysis Ranking, where hyperlink structures are used to determine the relative authority of a Web page and produce improved algorithms for the ranking of Web search results. In this article we work within the hubs and authorities framework defined by Kleinberg and we propose new families of algorithms. Two of the algorithms we propose use a Bayesian approach, as opposed to the usual algebraic and graph theoretic approaches. We also introduce a theoretical framework for the study of Link Analysis Ranking algorithms. The framework allows for the definition of specific properties of Link Analysis Ranking algorithms, as well as for comparing different algorithms. We study the properties of the algorithms that we define, and we provide an axiomatic characterization of the INDEGREE heuristic which ranks each node according to the number of incoming links. We conclude the article with an extensive experimental evaluation. We study the quality of the algorithms, and we examine how different structures in the graphs affect their performance.

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“…Several articles feature side-by-side comparisons among PageRank (and its extensions) and other metrics [42,33,30]. In particular, [26] focuses on comparing HITS, PageRank and SALSA, and its authors prove that PageRank is the only metric that guarantees algorithmic stability with every graph topology.…”

Section: Related Workmentioning

confidence: 99%

Black hole metric: Overcoming the pagerank normalization problem

Buzzanca

Carchiolo

Longheu

et al. 2018

Information Sciences

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In network science, there is often the need to sort the graph nodes. While the sorting strategy may be different, in general sorting is performed by exploiting the network structure. In particular, the metric PageRank has been used in the past decade in different ways to produce a ranking based on how many neighbors point to a specific node. PageRank is simple, easy to compute and effective in many applications, however it comes with a price: as PageRank is an application of the random walker, the arc weights need to be normalized. This normalization, while necessary, introduces a series of unwanted side-effects. In this paper, we propose a generalization of PageRank named Black Hole Metric which mitigates the problem. We devise a scenario in which the side-effects are particularily impactful on the ranking, test the new metric in both real and synthetic networks, and show the results.

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Perturbation of the Hyper-Linked Environment

Cited by 17 publications

References 11 publications

A Survey on PageRank Computing

A Survey on PageRank Computing

Link analysis ranking: algorithms, theory, and experiments

Black hole metric: Overcoming the pagerank normalization problem

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