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

By Ravi Kumar, D Sivakumar

This publication constitutes the refereed lawsuits of the seventh overseas Workshop on Algorithms and types for the Web-Graph, WAW 2010, held in Stanford, CA, united states, in December 2010, which was once co-located with the sixth foreign 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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Additional resources 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 and Counting Given Length Cycles. Algorithmica 17(3), 209–223 (1997) 2. : Counting triangles in large graphs using randomized matrix trace estimation. In: Proceedings of KDD-LDMTA 2010 (2010) 3. : Reductions in streaming algorithms, with an application to counting triangles in graphs. In: SODA (2002) 4. : Efficient Semi-Streaming Algorithms for Local Triangle Counting in Massive Graphs. In: KDD (2008) 5. : Counting Triangles in Data Streams. In: PODS (2006) 6. : A Note on an Inequality Involving the Normal Distribution.

Indeed, conditionally on the set W (vk ), the sets W (vi ) ∩ W (vj ), W (vi ) ∩ W (vk ), and W (vj ) ∩ W (vk ) are not mutually independent, and hence neither are the events {vi ↔ vj }, {vi ↔ vk }, and {vj ↔ vk }, that is, P[vi ↔ vj , vi ↔ vk , vj ↔ vk | W (vk )] = P[vi ↔ vj | W (vk )] ×P[vi ↔ vk | W (vk )] P[vj ↔ vk | W (vk )]. 4 Auxiliary Process on General Random Intersection Graphs Our analysis for the emergence of a giant component is inspired by the process described in [2], which measures the size of a component by counting the number of steps until a breadth-first search terminates.

A few vertices with negative holding power are expected, even after an optimal solution due to the noise. These results show that a composite similarity that resonates with a given clustering can be computed out of many metrics, none of which give a good solution by itself. In Table 1, we present these results on graphs with different number of vertices. While the percentages change for different number of vertices, our main conclusion that a good clustering can be achieved via a better aggregation function remains valid.

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