By Andrei Broder (auth.), Ravi Kumar, Dandapani Sivakumar (eds.)
This booklet constitutes the refereed complaints 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 foreign Workshop on net and community Economics (WINE 2010).
The thirteen revised complete papers and the invited paper provided have been conscientiously 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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Extra resources for Algorithms and Models for the Web-Graph: 7th International Workshop, WAW 2010, Stanford, CA, USA, December 13-14, 2010. Proceedings
Spectral Counting of Triangles via Element-Wise Sparsification and Triangle-Based Link Recommendation. In: ASONAM (2010) 34. : On the concentration of multivariate polynomials with small expectation. Random Structures and Algorithms 16(4), 344–363 (2000) 35. : Social Network Analysis: Methods and Applications (Structural Analysis in the Social Sciences). gov Abstract. 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.
We present experimental results on real and synthetic benchmarks. 1 Introduction A community or a cluster in a network is assumed to be a subset of vertices that are tightly coupled among themselves and loosely coupled with the rest of the network. Finding these communities is one of the fundamental problems of networks analysis and has been the subject of numerous research efforts. Most of these efforts begin with the premise that a simple graph is already constructed. That is the relation between two objects (hence existence of a single edge between two nodes) is already quantified with a binary variable or a single number that represents the strength of the connection.
Much larger clusters), then this would not have been the case, and having a separate quality of clustering metric would have made sense. However, we know that most complex networks have small communities no matter how big the graphs are . Therefore, we expect that looking only at the holding powers of vertices will be sufficient to recover aggregation functions. 3 Inverse Problems vs. Maximizing Clustering Quality We used the file system data set to investigate the relationship between the two proposed approaches, and present results in Fig.