Network deconvolution as a general method to distinguish direct dependencies in networks
Author(s)Feizi-Khankandi, Soheil; Marbach, Daniel; Medard, Muriel; Kellis, Manolis
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Nature Publishing Group
Feizi, Soheil, Daniel Marbach, Muriel Médard, and Manolis Kellis. “Network Deconvolution as a General Method to Distinguish Direct Dependencies in Networks.” Nature Biotechnology 31, no. 8 (July 14, 2013): 726–733.
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