"Algorithms and Models for the Web-Graph" - Information and Links:

Algorithms and Models for the Web-Graph - Info and Reading Options

5th International Workshop, WAW 2007, San Diego, CA, USA, December 11-12, 2007, Proceedings

"Algorithms and Models for the Web-Graph" was published by Springer London, Limited in 2007 - Berlin, Heidelberg, it has 1 pages and the language of the book is English.


“Algorithms and Models for the Web-Graph” Metadata:

  • Title: ➤  Algorithms and Models for the Web-Graph
  • Author:
  • Language: English
  • Number of Pages: 1
  • Publisher: Springer London, Limited
  • Publish Date:
  • Publish Location: Berlin, Heidelberg

“Algorithms and Models for the Web-Graph” Subjects and Themes:

Edition Specifications:

  • Pagination: x, 217

Edition Identifiers:

AI-generated Review of “Algorithms and Models for the Web-Graph”:


"Algorithms and Models for the Web-Graph" Description:

Open Data:

Intro -- Title page -- Preface -- Organization -- Table of Contents -- Bias Reduction in Traceroute Sampling -Towards a More Accurate Map of the Internet -- Introduction -- Our Contribution -- Related Work -- Outline of What Follows -- Estimation Technique -- Theoretical Analysis -- Computer Experiments -- Random Graph, Gn,m -- Preferential Attachment Graph -- Random Geometric Graph, G(X -- r) -- Western States Power Graph -- AS Graph -- Conclusion -- Distribution of PageRank Mass AmongPrinciple Components of the Web -- Introduction -- Datasets -- The Structure of the Hyper-link Transition Matrix -- PageRank Mass of IN+SCC -- PageRank Mass of ESCC -- Finding a Dense-Core in Jellyfish Graphs -- Introduction -- Background and Motivation -- Defining a Dense-Core -- Contributions -- Definitions and Notations -- The JellyCore Algorithm for Finding a Dense-Core in Jellyfish Graphs -- A Sublinear Algorithm -- Implementation -- Accuracy of the JellyCore Algorithm -- Execution Times -- Conclusions -- A Geometric Preferential Attachment Model ofNetworks II -- Introduction -- The Random Process -- Outline of the Paper -- Proving a Power Law -- Establishing a Recurrence for dk(t): The Expected Number of Vertices of Degree k at Time t -- Concentration of T(u) -- Concentration of dk(t) -- Small Separators -- Clustering Social Networks -- Introduction -- Related Work -- Combinatorics of (,)-Clusters -- An Algorithm for Finding Clusters with Champions -- Experiments -- Summary and Future Work -- Manipulation-Resistant Reputations UsingHitting Time -- Introduction -- Related Work -- Characterizing Hitting Time -- Preliminaries -- Theorem 1 -- Manipulation-Resistance -- Manipulating the Rankings -- Reputation and Influence of Sets -- Sybils -- Computing Hitting Time -- A Monte Carlo Algorithm -- Proofs -- Lemma 1 -- Lemma 2 -- Corollary 2 -- Corollary 3

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