Algorithms and Models for the Web-Graph: 6th International Workshop, WAW 2009 Barcelona, Spain, February 12-13, 2009, Proceedings
Konstantin Avrachenkov, Debora Donato, Nelly Litvak
Springer Science & Business Media, 26. sij 2009. - Broj stranica: 185
This volume constitutes the refereed proceedings of the 6th Workshop on - gorithms and Models for the Web Graph, WAW 2009, held in Barcelona in February 2009. The World Wide Web has become part of our everyday life, and information retrieval and data mining on the Web are now of enormous practical interest. The algorithms supporting these activities combine the view of the Web as a text repository and as a graph, induced in various ways by links among pages, links among hosts, or other similar networks. We also witness an increasing role of the second-generation Web-based applications Web 2.0, such as social networking sites and wiki sites. The workshop program consisted of 14 regular papers and two invited talks. The invited talks were given by Ravi Kumar (Yahoo! Research, USA) and Jos´ e Fernando Mendes (University of Aveiro, Portugal). The regular papers went through a thorough review process. The workshop papers were naturally cl- tered in three sections: “Graph Models for Complex Networks,” “PageRankand Web Graph” and “Social Networks and Search.” The ?rst section lays a foun- tionfor theoreticalandempiricalanalysisoftheWeb graphandWeb 2.0graphs.
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Graph Models for Complex Networks
Finding Dense Subgraphs with Size Bounds
The Giant Component in a Random Subgraph of a Given Graph
PageRank and Web Graph
Choose the Damping Choose the Ranking?
Characterization of Tail Dependence for InDegree and PageRank
Web Page Rank Prediction with PCA and EM Clustering
Social Networks and Search
Cluster Based Personalized Search
adjacency algorithm analysis applied approach approximation assume authors average bookmarking cluster colorful combined complex component compression compute consider contains core corresponds count damping data set deﬁned denote densest density dependencies described diﬀerent distance distribution documents edges engine evaluation exists experiments ﬁrst function given graph G Gray induced interest least Lemma length linear LNCS matrix measure method motifs negative networks nodes Note observe obtain PageRank parameters partitioning paths performance personalized positive prediction preference present probability problem produced proof properties proposed prove query random random graphs ranking relationships relevant represented respect returned score similar social steps structure subgraph tags Theorem types University vector vertex vertices walk