Algorithms and Models for the Web-Graph: 6th International by Kevin J. Lang (auth.), Konstantin Avrachenkov, Debora

By Kevin J. Lang (auth.), Konstantin Avrachenkov, Debora Donato, Nelly Litvak (eds.)

This booklet constitutes the refereed lawsuits of the sixth overseas Workshop on Algorithms and types for the Web-Graph, WAW 2009, held in Barcelona, Spain, in February 2009 - co-located with WSDM 2009, the second one ACM foreign convention on internet seek and information Mining.

The 14 revised complete papers provided have been conscientiously reviewed and chosen from a variety of submissions for inclusion within the ebook. The papers deal with a wide selection of issues relating to the examine of the Web-graph comparable to theoretical and empirical research of the net graph and net 2.0 graphs, random walks on the internet and internet 2.0 graphs and their purposes, and layout and function overview of the algorithms for social networks. The workshop papers were clearly clustered in 3 topical sections on graph versions for complicated networks, pagerank and net graph, and social networks and search.

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

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If I ≥ k, then HI satisfies the requirements of the theorem. If I < k, then C(2d∗ /3) (G) = HI is contained in Hk , and the following calculation shows that Hk satisfies the requirements of the theorem. d(Hk ) = W (C(2d∗ /3) (G)) W (Hk ) W (H∗ )/3 ≥ ≥ = d∗ /3. k k k ⊓ ⊔ We remark that our analysis of FindLargeDenseSubgraph is a generalization of the result of Kortsarz-Peleg [18]. Their result shows that FindLargeDenseSubgraph(G, 1) is a (1/2)-approximation algorithm for ds. This follows from the fact that if w = Dmax (G), then the w-core of G is nonempty, which is a special case of Lemma 2.

Special Issue of ECCS 2007 in The Journal of Networks and Heterogeneous Media 3(2), 239–249 (2008) 10. : Not all scale-free networks are born equal: The role of the seed graph in ppi network evolution. PLoS: Computational Biology 3(7), e118 (2007) 11. : Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20(11), 1746 (2004) 12. : Network motifs: Simple building blocks of complex networks. Science 298, 824–827 (2002) 13. : Biological network comparison using graphlet degree distribution.

Percolation within the critical window on random regular graphs is also studied by Nachmias and Peres in [17]. Our results differ from these in that we study percolation on graphs with a much more general degree sequence. The greater preciseness of these results, however, is quite desirable. It is an interesting open question to describe the precise scaling window for percolation for the more general graphs studied here. ) Compared with earlier results, the main advantage of our results is the ability to handle general degree sequences.

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