In a seminal paper in 2009, Borcea, Brändén, and Liggett described the connection between probability distributions and the geometry of their generating polynomials. Namely, they characterized that stable generating polynomials correspond to distributions with the strongest form of negative dependence. This motivates us to investigate other distributions that can have this property, and our focus is on random graph models. In this article, we will lay the groundwork to investigate Markov random graphs, and more generally exponential random graph models (ERGMs), from this geometric perspective. In particular, by determining when their corresponding generating polynomials are either stable and/or Lorentzian. The Lorentzian property was first described in 2020 by Brändén and Huh and independently by Anari, Oveis-Gharan, and Vinzant where the latter group called it the completely log-concave property. The theory of stable polynomials predates this, and is commonly thought of as the multivariate notion of real-rootedness. Brändén and Huh proved that stable polynomials are always Lorentzian. Although it is a strong condition, verifying stability is not always feasible. We will characterize when certain classes of Markov random graphs are stable and when they are only Lorentzian. We then shift our attention to applications of these properties to real-world networks.