By Andrei Broder (auth.), Ravi Kumar, Dandapani Sivakumar (eds.)
This ebook constitutes the refereed complaints 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 was once co-located with the sixth foreign Workshop on net and community Economics (WINE 2010).
The thirteen revised complete papers and the invited paper offered have been rigorously reviewed and chosen from 19 submissions.
Read 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
Rev. E 70(5), 056131 (2004) 9. : Modularity and community structure in networks. PNAS 103, 8577–8582 (2006) 10. 0 user manual. Technical Report SAND2009-6265, Sandia National Laboratories (2009) 11. : Performance criteria for graph clustering and markov cluster experiments. Technical Report INS-R0012, Centre for Mathematics and Computer Science (2000) Component Evolution in General Random Intersection Graphs Milan Bradonji´c1 , Aric Hagberg2, Nicolas W. Hengartner3, and Allon G. edu Abstract. Random intersection graphs (RIGs) are an important random structure with algorithmic applications in social networks, epidemic networks, blog readership, and wireless sensor networks.
This will not be easy for two reasons. First, there is no perfect clustering algorithm, and secondly, even if we were able to solve the clustering problem optimally, we would not have the exact objective function for clustering. Also, the need to solve many clustering problems will be time-consuming especially for large graphs. 2 Maximizing the Quality of Ground-Truth Clustering An alternative approach is to find an aggregation function that maximizes the quality of the ground-truth clustering.
For this figure we compute the objective function for the ground-truth clustering for various aggregation weights and use the same weights to compute clusterings with Graclus. From these clusterings we compute the variation of information (VI) distance to the ground-truth. Fig. 5 presents the correlation between the measures: VI distance for Graclus clusterings for the first approach, and the objective function values for the second approach. This tries to answer whether solutions with higher objective function values yield clusterings closer to the ground-truth using Graclus.