Algorithms and Models for the Web-Graph 6th International Workshop, WAW 2009 Barcelona, Spain, February 12-13, 2009, Proceedings /

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 pa...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Avratchenkov, Konstantin (Editor ), Donato, Debora (Editor ), Litvak, Nelly (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2009.
Edición:1st ed. 2009.
Colección:Theoretical Computer Science and General Issues, 5427
Temas:
Algorithms.
Computer science-Mathematics.
Discrete mathematics.
Application software.
Data mining.
Computer networks .
Database management.
Discrete Mathematics in Computer Science.
Computer and Information Systems Applications.
Data Mining and Knowledge Discovery.
Computer Communication Networks.
Database Management.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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.

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