By Evimaria Terzi, Marco Winkler (auth.), Alan Frieze, Paul Horn, Paweł Prałat (eds.)
This ebook constitutes the refereed lawsuits of the eighth overseas Workshop on Algorithms and types for the Web-Graph, WAW 2011, held in Atlanta, GA, in may well 2011 - co-located with RSA 2011, the fifteenth foreign convention on Random constructions and Algorithms.
The thirteen revised complete papers awarded including 1 invited lecture have been conscientiously reviewed and chosen from 19 submissions. Addressing a large choice of issues with regards to the examine of the Web-graph resembling theoretical and empirical research, the papers function unique learn when it comes to algorithmic and mathematical research in all components relating the World-Wide net with distinct concentration to the view of complicated info as networks.
Read or Download Algorithms and Models for the Web Graph: 8th International Workshop, WAW 2011, Atlanta, GA, USA, May 27-29, 2011. Proceedings PDF
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Extra info for Algorithms and Models for the Web Graph: 8th International Workshop, WAW 2011, Atlanta, GA, USA, May 27-29, 2011. Proceedings
Example text
Neither of the two metrics can provide the full information for the underlying data. However when considered as an ensemble they do provide a complete picture. Our goal is to be able to tease out the latent factors of data from a given set of partial views. In this paper, we will use this 3 × 3 example for conceptual purposes and for illustrations. Our approach is construct many multi-weighted graphs by using combinations of the partial views of the data. We will cluster these graphs and analyze these clusters to recover the latent structure.
These are ordered to maximize cumulative set-wise information. Notice how the first two representative-clusterings recover the original nine clusterings exactly. with little error. We see also that the two factors are identified perfectly by each of these clusterings individually. 2 Ordering by Set-Wise Information Content In Fig. 4, the original 3x3 community structure can be reconstructed using only the first two representative-clusterings. Why are these two chosen first? Selecting the third and fourth representative-clusterings would not have had this pleasant result.
We may apply a traditional clustering algorithm C to this graph to obtain a clustering C(Gαj ) = Cαj . This process identifies with each point αj ∈ Rk a clustering Cαj . Thus a multiweighted graph is imbued with a space of clusterings. Latent Clustering on Graphs with Multiple Edge Types 43 We expect that different regions of this space will have different clusterings. How drastic these differences are will depend on the particular multiweighted graph. How can we characterize this space of clusterings?
