Cortical stimulation mapping (CSM) is a common clinical procedure for mapping eloquent cortex in epilepsy patients. Electrical responses to the stimulation, or after-discharges (ADs), that occur in response to stimulation can point to unstable regions of cortex that are more prone to spontaneous seizures. Clinicians are interested in identifying regions that start seizures, i.e., the epileptogenic zone (EZ), so that they can target treatment. However, during CSM, not all regions are stimulated, as it would be time-consuming and potentially harmful to the patient. This limits the clinician's ability to fully explore ADs to reliably localize the EZ. In this paper, we develop a virtual CSM procedure that processes pre-seizure intracranial EEG recordings obtained from epilepsy patients being treated at three different epilepsy centers. First, we identify a linear time varying network (LTVN) model from electrocorticography (ECoG) and stereo-EEG (SEEG) data using sparse least squares estimation for each patient. We then construct an virtual CSM by applying impulse perturbations to each electrode contact in the LTVN model and then measuring the ADs of the network. We summarize the l2-norm of the responses in the form of a heatmap that shows the spatio-temporal evolution of the ADs before, during, and after seizures. Finally we compute an impulse response ratio (IRR) metric from each heatmap, that measures the ratio between the mean norm of ADs of clinically annotated EZ contacts and the mean norm of ADs of the remaining contacts. We find that the IRR is higher in maps derived from patients with successful surgical outcomes and lower in failed surgical outcomes. This suggests that virtual CSM may provide valuable information to clinicians regarding EZ location.