Screens monitoring the effects of deletion, knock-down or over expression of regulatory genes on the expression of their target genes are critical for deciphering the organization of complex regulatory networks. However, since perturbation assays cannot distinguish direct from indirect effects, the derived networks are significantly more complex than the true underlying one. Discovery of the true network organization is a long-standing challenge and several approaches have been developed to infer regulatory networks based on gene expression data. Recent studies indicate [ref] that information obtained from perturbation screens is critical for the ability to identify network structure accurately. This information is typically incorporated into network inference algorithms in two ways: first, the strength of the perturbation effect is translated into interaction confidence values and, second, topological analysis of the experimental networks is performed to find interactions that are most important to preserve network structure and, hence, are more likely to be biological. This underscores importance of having accurate methodology for accurate inference of network topology. In this work we present Exigo, an approach for systematic analysis of network topology. Exigo provides the means to identify core network structure for an input network of any topology with an arbitrary number of activating and inhibiting interactions. We further show that Exigo allows for significant improvement in the network inference. To illustrate this, we constructed a chimeric network inference method that incorporates Exigo into existing inference pipeline [ref], benchmarked it against DREAM challenge networks and found significant improvement in network inference compared to DREAM top performers.