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 overseas Workshop on web and community Economics (WINE 2010). The thirteen revised complete papers and the invited paper offered have been conscientiously reviewed and chosen from 19 submissions.

**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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This ebook constitutes the refereed lawsuits of the seventh overseas 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 foreign Workshop on web and community Economics (WINE 2010). The thirteen revised complete papers and the invited paper offered have been conscientiously 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 **

**Sample text**

After the noise, none of the original metrics preserved the original clustering structure. We display this in Fig. 2, which presents histograms for the holding power for vertices. The green bars correspond to vertices of the original graph, they all have positive holding power. The blue bars correspond to holding powers after noise is added. Computing an Aggregate Edge-Weight Function 31 Fig. 2. Three histograms of holding powers for Blue: an example perturbed (poor) edge type, Green: the original data (very good), Red: the optimal blend of ten of the perturbed edge types We only present one edge type for clarity of presentation.

30 M. Rocklin and A. Pinar k Then the objective function can be rewritten as minα K 1 αk S . Because this is linear it has a trivial solution that assigns 1 to the weight of the maximum S k , which means only one similarity metric is taken into account. While additional constraints may exclude this specific solution, a linear formulation of the quality will always yield only a trivial solution within the new feasible region. In our experiments we used the modularity metric [9]. The modularity metric uses a random graph generated with respect to the degree distribution as the null hypothesis, setting the modularity score of a random clustering to 0.

1. A set of edges E ∈ V × V is then defined, such that for two different nodes vi , vj ∈ V , {vi , vj } ∈ E iff |W (vi ) ∩ W (vj )| ≥ s (3) V v1 v2 v3 (2) W p1 p2 pm w1 w2 w3 for a given integer s ≥ 1. Thus, two nodes are adjacent if and only if they p1 have at least s attributes in common. p2 vn One limitation of our analysis is that pm for simplicity, we fix s = 1. wm Our model generalizes the uniform model G(n, m, p), studied in [4,6], where all pw take on the same value p. Different generalizations and special Fig.