By Kevin J. Lang (auth.), Konstantin Avrachenkov, Debora Donato, Nelly Litvak (eds.)
This publication constitutes the refereed complaints of the sixth foreign Workshop on Algorithms and versions 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 net seek and information Mining.
The 14 revised complete papers offered have been rigorously reviewed and chosen from quite a few submissions for inclusion within the booklet. The papers handle a large choice of themes concerning the learn of the Web-graph reminiscent of theoretical and empirical research of the net graph and net 2.0 graphs, random walks on the internet and net 2.0 graphs and their purposes, and layout and function evaluate of the algorithms for social networks. The workshop papers were obviously clustered in 3 topical sections on graph versions for advanced networks, pagerank and internet graph, and social networks and search.
Read Online or Download Algorithms and Models for the Web-Graph: 6th International Workshop, WAW 2009, Barcelona, Spain, February 12-13, 2009. Proceedings PDF
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This publication constitutes the refereed court cases of the sixth overseas Workshop on Algorithms and versions 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 net seek and knowledge Mining. The 14 revised complete papers provided have been rigorously reviewed and chosen from various submissions for inclusion within the publication.
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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
VI(w)+1 that were removed before vI(w) can be contained in an induced subgraph with minimum degree at least w. That implies Cw (G) ⊆ HI(w) . On the other hand, the minimum degree of HI(w) is at least w, so HI(w) ⊆ Cw (G). Therefore, HI(w) = Cw (G). ⊓ ⊔ 30 R. Andersen and K. Chellapilla Lemma 2. For any graph G with n nodes, total weight W , and density d = W/n, the d-core of G is nonempty. Furthermore, for any α ∈ [0, 1], the total weight of the (αd)-core of G is strictly greater than (1 − α)W .
K. Avrachenkov, D. Donato, and N. ): WAW 2009, LNCS 5427, pp. 25–37, 2009. c Springer-Verlag Berlin Heidelberg 2009 26 R. Andersen and K. Chellapilla The complexity of identifying dense subgraphs can vary greatly when additional constraints on the size of the subgraph are introduced. Finding the densest subgraph with an arbitrary number of vertices is known as the densest subgraph problem ds, and can be solved exactly in polynomial time by solving a sequence of maximum ﬂow problems [15,13]. The algorithm of Kortsarz and Peleg  produces a (1/2)-approximation of the densest subgraph in linear time, which is useful for graphs where the time required to compute maximum ﬂows is prohibitively large.
Bioinformatics 150, 216–231 (2005) 15. : Eﬃcient algorithms for detecting signaling pathways in protein interaction networks. , Waterman, M. ) RECOMB 2005. LNCS (LNBI), vol. 3500, pp. 1–13. Springer, Heidelberg (2005) 16. : QPath: a method for querying pathways in a protein-protein interaction network. Bioinformatics 7, 199 (2006) 17. : Eﬃcient detection of network motifs. com Abstract. We consider the problem of ﬁnding dense subgraphs with speciﬁed upper or lower bounds on the number of vertices.
Algorithms and Models for the Web-Graph: 6th International Workshop, WAW 2009, Barcelona, Spain, February 12-13, 2009. Proceedings by Kevin J. Lang (auth.), Konstantin Avrachenkov, Debora Donato, Nelly Litvak (eds.)