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 court cases 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 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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By Ravi Kumar, D Sivakumar

This e-book constitutes the refereed court cases 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 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.

Show description

Read Online or Download Algorithms and Models for the Web-Graph: 7th International Workshop, WAW 2010, Stanford, CA, USA, December 13-14, 2010, Proceedings PDF

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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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1. We can solve the following optimization problem to maximize the number of vertices, whose positions in the ground-truth clustering are justified by the weighting vector α. Fig. 1. Arctangent provides a smooth blend between step and linear functions arctan(βHα (v)) arg max α∈RK (3) v∈V Overall Clustering Quality. In addition to individual vertices being justified, overall quality of the clustering should be maximized. Any quality metric can potentially be used for this purpose however we find that some strictly linear functions have a trivial solution.

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.

Subsequently, we replace the weight of an edge wi ∈ R with a weight vector wi1 , wi2 , . . , wiK ∈ RK , where Kis the number of different edge types. A composite similarity can be defined by a function RK → R to reduce the weight vector to a single number. In this paper, we will restrict ourselves to linear functions such that the composite edge weight wi (α) K is defined as j=1 αj wij . 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.

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