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Multivariate construction of effective computational networks from observational data
Joseph Lizier and Mikail Rubinov
We introduce a new method for inferring effective network structure given a multivariate time-series of activation levels of the nodes in the network. For each destination node in the network, the method identifies the set of source nodes which can be used to provide the most statistically significant information regarding outcomes of the destination, and are thus inferred as those source information nodes from which the destination is computed. Our method is model-free, non-linear and most importantly handles multivariate interactions between sources in creating outcomes at destinations, while incorporating measures to avoid combinatorial explosions in the number of source combinations evaluated. We apply the method to probabilistic Boolean networks (serving as models of Gene Regulatory Networks), demonstrating the utility of the method in revealing significant proportions of the underlying structural network given only short time-series of the network dynamics, particularly in comparison to other methods.