Based on a recent development in the area of error control coding, we introduce the notion of convolutional factor graphs (CFGs) as a new class of probabilistic graphical models. ...
The title of this paper, besides being a pun, can be taken to mean either the frontier of research in graph transformation, or the advantage of using graph transformation. To focus...
The vast majority of earlier work has focused on graphs which are both connected (typically by ignoring all but the giant connected component), and unweighted. Here we study numer...
Many techniques in the social sciences and graph theory deal with the problem of examining and analyzing patterns found in the underlying structure and associations of a group of ...
Jeremy Kubica, Andrew W. Moore, David Cohn, Jeff G...
This paper addresses the problem of learning archetypal structural models from examples. To this end we define a generative model for graphs where the distribution of observed nod...