New PDF release: Algorithms and Models for the Web-Graph: 7th International

By Ravi Kumar, D Sivakumar

ISBN-10: 3642180086

ISBN-13: 9783642180088

This e-book constitutes the refereed court cases 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 foreign Workshop on net and community Economics (WINE 2010). The thirteen revised complete papers and the invited paper provided 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

Example text

1 Clustering in Graphs Intuitively, the goal of clustering is to break down the graph into smaller groups such that vertices in each group are tightly coupled among themselves, and loosely coupled with the remainder of the network. Both the translation of this intuition into a welldefined mathematical formula and design of associated algorithms pose big challenges. Despite the high quality and the high volume of the literature, the area continues to draw a lot of interest both due to the growing importance of the problem and the challenges posed by the sizes of the subject graphs and the mathematical variety as we get into the details of these problems.

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.

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. : Concentration of multivariate polynomials and its applications. Combinatorica 20(3), 417–434 (2000) 20. : Seminumerical Algorithms, 3rd edn. N. Kolountzakis et al. 21. 0468 22. : Main-memory triangle computations for very large (sparse (power-law)) graphs.

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Algorithms and Models for the Web-Graph: 7th International Workshop, WAW 2010, Stanford, CA, USA, December 13-14, 2010, Proceedings by Ravi Kumar, D Sivakumar

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