Download Algorithms and Models for the Web-Graph: 7th International by Andrei Broder (auth.), Ravi Kumar, Dandapani Sivakumar PDF

By Andrei Broder (auth.), Ravi Kumar, Dandapani Sivakumar (eds.)

This publication constitutes the refereed court cases 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 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 awarded have been rigorously reviewed and chosen from 19 submissions.

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Additional 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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Component evolution in a secure wireless sensor network. Netw. 53(1), 19–26 (2009) 7. : The average distances in random graphs with given expected degrees. Proceedings of the National Academy of Sciences of the United States of America 99(25), 15879–15882 (2002) 8. : Random intersection graphs with tunable degree distribution and clustering. Probab. Eng. Inf. Sci. 23(4), 661–674 (2009) 9. : The representation of a graph by set intersections. Canad. J. Math. 18, 106–112 (1966) 10. : Random intersection graphs and classification.

523–534. Springer, Heidelberg (2008) 24. : Measurement and Analysis of Online Social Networks. In: IMC (2007) 25. : The structure and function of complex networks (2003) 26. : The clique problem for planar graphs. 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.

We investigate the community detection problem on graphs in the existence of multiple edge types. Our main motivation is that similarity between objects can be defined by many different metrics and aggregation of these metrics into a single one poses several important challenges, such as recovering this aggregation function from ground-truth, investigating the space of different clusterings, etc. In this paper, we address how to find an aggregation function to generate a composite metric that best resonates with the ground-truth.

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