By Ravi Kumar, D Sivakumar

This publication constitutes the refereed lawsuits of the seventh foreign Workshop on Algorithms and types 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 web 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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Pareto frontier for two objectives: normalized modularity and percentage of nodes with positive holding power with respect to the modularity metric [9], while the horizontal axis represents the percentage of nodes with positive holding power. The modularity numbers are normalized with respect to the modularity of the ground-truth clustering, and normalized numbers can be above 1, since the ground-truth clustering does not specifically aim at maximizing modularity. As expected, Fig. 4 shows a trade-off between two objectives.

Neural Computation 16, 1299–1323 (2004) 4. : Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. Internet Mathematics 6, 29–123 (2009) 5. : On coreference resolution performance metrics. In: Proc. Human Language Technology Conf. and Conf. Empirical Methods in Natural Language Processing, Vancouver, British Columbia, Canada, pp. 25–32. Association for Computational Linguistics (2005) 6. : Comparing clusterings: an axiomatic view. In: Proceedings of the 22nd International Conference on Machine Learning, 2005, pp.

Information Processing Letters 13, 131–133 (1981) 27. : Finding, Counting and Listing all Triangles in Large Graphs, An Experimental Study. E. ) WEA 2005. LNCS, vol. 3503, pp. 606–609. Springer, Heidelberg (2005) 28. : Approximating Clustering Coefficient and Transitivity. Journal of Graph Algorithms and Applications 9, 265–275 (2005) 29. : Fast Counting of Triangles in Large Real Networks, without counting: Algorithms and Laws. In: ICDM (2008) 30. : Counting Triangles Using Projections. KAIS Journal (2010) 31.

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