[NRG] Notification: Decomposing Network Data into Generating Subnetworks (Bas... @ Mon Sep 26, 2016 11am - 12pm (NRG at BU)

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Sun Sep 25 10:59:48 EDT 2016

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Title: Decomposing Network Data into Generating Subnetworks (Bashir  
Low-rank matrix factorization methods are standard tools in data analysis.  
They provide a low dimensional representation of a data matrix by revealing  
latent factors that can explain the underlying structure of the data. Many  
modern data mining problems tackle network data where, in addition to the  
attribute vectors of data points, relational information of data points are  
also available in the form a network. Extending the classical factorization  
techniques to methods that take the network information into account is  
hence a natural problem. We formulate a new matrix factorization problem  
that decomposes a data matrix together with an associated network of data  
points into connected subnetworks, each of which associated with a  
signature attribute vector. Each observed data point can then be expressed  
as a sum over the of signature vectors of the subnetworks that contain that  
data point. We provide an alternating algorithm for solving our matrix  
factorization that is based on a transformation to the Prize Collecting  
Steiner Tree problem. We apply our algorithms on both synthetic and  
real-world data sets and show that minimizing the factorization error can  
reveal interesting information about the processes that generate the  
observed data matrix. In synthetic data, we observe that our algorithms can  
retrieve planted subnetworks with high accuracy. In the case of real-world  
data, where the ground truth is unknown, we observe that detected  
subnetworks show interesting clusters of nodes whose bahavior can be well  
described by the associated signature vector of each subnetwork.
When: Mon Sep 26, 2016 11am – 12pm Eastern Time
Calendar: NRG at BU
     * Cody Doucette - creator

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