First order random graphs as introduced by Wong are a promising tool for structure-based classi$cation. Their complexity, however, hampers their practical application. We describe an extension to $rst order random graphs which uses continuous Gaussian distributions to model the densities of all random elements in a random graph. These First Order Gaussian Graphs (FOGGs) are shown to have severa...
