By Kevin J. Lang (auth.), Konstantin Avrachenkov, Debora Donato, Nelly Litvak (eds.)
This ebook constitutes the refereed complaints of the sixth foreign 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 overseas convention on internet seek and knowledge Mining.
The 14 revised complete papers offered have been rigorously reviewed and chosen from a variety of submissions for inclusion within the booklet. The papers deal with a large choice of themes relating to the learn of the Web-graph similar to theoretical and empirical research of the internet graph and net 2.0 graphs, random walks on the net and internet 2.0 graphs and their purposes, and layout and function review of the algorithms for social networks. The workshop papers were clearly clustered in 3 topical sections on graph types for advanced networks, pagerank and net 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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Extra info for Algorithms and Models for the Web-Graph: 6th International Workshop, WAW 2009, Barcelona, Spain, February 12-13, 2009. Proceedings
Percolation on ﬁnite graphs and isoperimetric inequalities. Annals of Probability 32(3), 1727–1745 (2004) 2. : Largest random component of a k-cube. Combinatorica 2, 1–7 (1982) 3. : The evolution of random subgraphs of the cube. Random Structures and Algorithms 3(1), 55–90 (1992) 4. : Percolation on dense graph sequences (preprint) 5. : Random subgraphs of ﬁnite graphs. I. The scaling window under the triangle condition. Random Structures and Algorithms 27(2), 137–184 (2005) 6. : Random subgraphs of ﬁnite graphs.
Algorithm 6. (Algorithm for counting 4-cliques that are adjacent to v) 1. Cl(v) = 0. 2. For every vertex u ∈ N (v): Approximating the Number of Network Motifs 23 (a) Compute N (v) ∩ N (u): i. Go over all the vertices in the adjacency list of v and the adjacency list of u, and add each vertex to a list. (Thus a vertex can appear several times in the list). ii. Sort the vertices in the list (which is a multiset) according the names of the vertices. iii. For each vertex in the list count the number of times it appears in the list.
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. We introduce two optimization problems: the densest at-least-k-subgraph problem (dalks), which is to ﬁnd an induced subgraph of highest average degree among all subgraphs with at least k vertices, and the densest at-most-k-subgraph problem (damks), which is deﬁned similarly.