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Network deconvolution as a general method to distinguish direct dependencies in networks

Network Journal Club, University of Oxford
When Oct 09, 2013
from 12:00 PM to 01:00 PM
Where Andrew Wiles Building, seminar room N3.12
Contact Name Mikko Kivelä
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Speaker: Florian Klimm
Article details:
Title: Network deconvolution as a general method to distinguish direct dependencies in networks
Authors: Soheil Feizi, Daniel Marbach, Muriel Médard, & Manolis Kellis
Abstract: Recognizing direct relationships between variables connected in a network is a pervasive problem in biological, social and information sciences as correlation-based networks contain numerous indirect relationships. Here we present a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects. We formulate the problem as the inverse of network convolution, and introduce an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums. We demonstrate the effectiveness of our approach in several network applications: distinguishing direct targets in gene expression regulatory networks; recognizing directly interacting amino-acid residues for protein structure prediction from sequence alignments; and distinguishing strong collaborations in co-authorship social networks using connectivity information alone. In addition to its theoretical impact as a foundational graph theoretic tool, our results suggest network deconvolution is widely applicable for computing direct dependencies in network science across diverse disciplines.