New PDF release: Algorithms and Models for the Web-Graph: 5th International

By Abraham D. Flaxman, Juan Vera (auth.), Anthony Bonato, Fan R. K. Chung (eds.)

ISBN-10: 3540770038

ISBN-13: 9783540770039

This publication constitutes the refereed complaints of the fifth foreign Workshop on Algorithms and types for the Web-Graph, WAW 2007, held in San Diego, CA, united states, in December 2007 - colocated with WINE 2007, the 3rd foreign Workshop on web and community Economics.

The thirteen revised complete papers and 5 revised brief papers awarded have been rigorously reviewed and chosen from a wide pool of submissions for inclusion within the ebook. The papers tackle a large choice of themes on the topic of the learn of the Web-graph similar to random graph types for the Web-graph, PageRank research and computation, decentralized seek, neighborhood partitioning algorithms, and traceroute sampling.

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Read Online or Download Algorithms and Models for the Web-Graph: 5th International Workshop, WAW 2007, San Diego, CA, USA, December 11-12, 2007. Proceedings PDF

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Additional info for Algorithms and Models for the Web-Graph: 5th International Workshop, WAW 2007, San Diego, CA, USA, December 11-12, 2007. Proceedings

Sample text

On the power law random graph model of the Internet. Performance Evaluation 55 (January 2004) 29. : A clustering approach for exploring the Internet structure. In: Proc. 23rd IEEE Convention of Electrical & Electronics Engineers in Israel (IEEEI) (2004) 30. : DIMES: Let the Internet measure itself. In: Proc. ACM SIGCOMM, pp. 71–74 (2005) 31. : Jellyfish: A conceptual model for the as Internet topology. Journal of Communications and Networks (2006) 32. : Characterizing the Internet hierarchy from multiple vantage points.

Our algorithm and its analysis take some ideas from the Approximate-Clique Finding Algorithm of [19] (which is designed for dense graphs). The algorithm is given query access to the graph G, and takes as input: k (the requested dense-core size), d (the minimal degree for nodes in the nucleus), , and a sample size s. 34 M. Gonen et al. Algorithm 1. (The JellyCore algorithm for approximating C and H) 1. Uniformly and independently at random select s vertices. Let S be the set of vertices selected.

For a set of vertices X, let Γ (X) denote the set of vertices that neighbor at least one vertex in X, and let Γδ (X) denote the set of vertices that neighbor all but at most δ|X| vertices in X. We next introduce our main definition. Definition 4. t. the following conditions hold: 1. For all v ∈ C, v neighbors at least (1 − )|H| vertices in H, 2. For all but |Γ3 (H)| vertices, if a vertex v ∈ V neighbors at least (1 − )|H| vertices in H then v has at least (1 − )|C| neighbors in C. Finding a Dense-Core in Jellyfish Graphs 33 V \C C H Fig.

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Algorithms and Models for the Web-Graph: 5th International Workshop, WAW 2007, San Diego, CA, USA, December 11-12, 2007. Proceedings by Abraham D. Flaxman, Juan Vera (auth.), Anthony Bonato, Fan R. K. Chung (eds.)


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