Algorithms and Models for the Web-Graph (eBook, PDF)

6th International Workshop, WAW 2009 Barcelona, Spain, February 12-13, 2009, Proceedings
Redaktion: Avratchenkov, Konstantin; Litvak, Nelly; Donato, Debora

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the 6th International Workshop on Algorithms and Models for the Web-Graph, WAW 2009, held in Barcelona, Spain, in February 2009 - co-located with WSDM 2009, the Second ACM International Conference on Web Search and Data Mining.

The 14 revised full papers presented were carefully reviewed and selected from numerous submissions for inclusion in the book. The papers address a wide variety of topics related to the study of the Web-graph such as theoretical and empirical analysis of the Web graph and Web 2.0 graphs, random walks on the Web and Web 2.0 graphs and their applications, and design and performance evaluation of the algorithms for social networks. The workshop papers have been naturally clustered in three topical sections on graph models for complex networks, pagerank and Web graph, and social networks and search.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 185
• Erscheinungstermin: 30. Januar 2009
• Englisch
• ISBN-13: 9783540959953
• Artikelnr.: 44127533

Inhaltsangabe

Graph Models for Complex Networks.- Information Theoretic Comparison of Stochastic Graph Models: Some Experiments.- Approximating the Number of Network Motifs.- Finding Dense Subgraphs with Size Bounds.- The Giant Component in a Random Subgraph of a Given Graph.- Quantifying the Impact of Information Aggregation on Complex Networks: A Temporal Perspective.- PageRank and Web Graph.- A Local Graph Partitioning Algorithm Using Heat Kernel Pagerank.- Choose the Damping, Choose the Ranking?.- Characterization of Tail Dependence for In-Degree and PageRank.- Web Page Rank Prediction with PCA and EM Clustering.- Permuting Web Graphs.- Social Networks and Search.- A Dynamic Model for On-Line Social Networks.- TC-SocialRank: Ranking the Social Web.- Exploiting Positive and Negative Graded Relevance Assessments for Content Recommendation.- Cluster Based Personalized Search.