Download Algorithms and Models for the Web-Graph: 7th International by Ravi Kumar, D Sivakumar PDF

By Ravi Kumar, D Sivakumar

This e-book constitutes the refereed complaints of the seventh foreign Workshop on Algorithms and versions for the Web-Graph, WAW 2010, held in Stanford, CA, united states, in December 2010, which used to be co-located with the sixth overseas Workshop on net and community Economics (WINE 2010). The thirteen revised complete papers and the invited paper offered have been rigorously reviewed and chosen from 19 submissions.

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Extra info for Algorithms and Models for the Web-Graph: 7th International Workshop, WAW 2010, Stanford, CA, USA, December 13-14, 2010, Proceedings

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Finding a minimum circuit in a graph. In: STOC (1977) 15. : Extensions of Lipschitz mappings into a Hilbert space. Contemporary Mathematics 26, 189–206 (1984) 16. : New Streaming Algorithms for Counting Triangles in Graphs. In: Wang, L. ) COCOON 2005. LNCS, vol. 3595, pp. 710–716. Springer, Heidelberg (2005) 17. : Radius Plots for Mining Tera-byte Scale Graphs: Algorithms, Patterns, and Observations. In: SIAM Data Mining, SDM 2010 (2010) 18. : PEGASUS: A Peta-Scale Graph Mining System. In: IEEE Data Mining, ICDM 2009 (2009) 19.

It emphasises nodes which are close to the H(v) = 0 crossing point (large gradients) over nodes which are well entrenched (low gradients near extremes). This objective function sacrifices holding scores for nodes which are safely entrenched in their cluster (high holding power) or are lost causes (very low holding power) for those which are near the cross-over point. The extent to which it does this can be tuned by β, the steepness parameter of the arctangent. For very steep parameters this function resembles classification (step function) while for very shallow parameters it resembles a simple linear sum as seen in Fig.

Hengartner3, and Allon G. edu Abstract. Random intersection graphs (RIGs) are an important random structure with algorithmic applications in social networks, epidemic networks, blog readership, and wireless sensor networks. RIGs can be interpreted as a model for large randomly formed non-metric data sets. We analyze the component evolution in general RIGs, giving conditions on the existence and uniqueness of the giant component. Our techniques generalize existing methods for analysis of component evolution: we analyze survival and extinction properties of a dependent, inhomogeneous Galton-Watson branching process on general RIGs.

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