[Statlist] Friday, April 29, 2016 with Christian Brownless (Universität Pompeu Fabra), Barcelona

[Maurer Letizia]

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ETH and University of Zurich

Organisers:

Proff. P. Bühlmann - L. Held - T. Hothorn - M. Maathuis -
N. Meinshausen - S. van de Geer - M. Wolf
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We are glad to announce the following talk:

Friday, April 29, 2016 at 15.15h  ETH Zurich HG G 19.141
with Christian Brownless (Universität Pompeu Fabra), Barcelona
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Title:

Community Detection in Partial Correlation Network Models

Abstract:

Real world networks often exhibit a community structure, in the sense that the vertices of the network are partitioned into groups such that the concentration of linkages is high among vertices in the same group and low otherwise. This moti- vates us to introduce a class of Gaussian graphical models whose network structure is determined by a stochastic block model. The stochastic block model is a random graph in which vertices are partitioned into communities and the existence of a link between two vertices is determined by a Bernoulli trial with a probability that de- pends on the communities the vertices belong to. A natural question that arises in this framework is how to detect communities from a random sample of observations. We introduce a community-detection algorithm called Blockbuster, which consists of applying spectral clustering to the sample covariance matrix, that is, it applies k-means clustering to the eigenvectors corresponding to its largest eigenvalues. We study the properties of the procedure and show that Blockbuster consistently de- tects communities when the network dimension and the sample size are large. The methodology is used to study real activity clustering in the United States and Eu- rope.
Keywords: Partial Correlation Networks, Random Graphs, Community Detection, Spec- tral Clustering, Graphical Models
JEL: C3, C33, C55


This abstract is also to be found under the following link: http://stat.ethz.ch/events/research_seminar

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