This dissertation develops statistical inference methods for graphical models where the graph structure has been learned using the data. Standard inference methods assume a given model and become invalid after data-drive edge selection. The thesis addresses this issue through selective inference, which conditions on the selection event and restores valid type I error rate control. The main contributions are an extension of polyhedral selective inference to regularized graphical models, including