A Bayesian treatment of latent directed graph structure for non-iid data is provided where each child datum is sampled with a directed conditional dependence on a single unknown p...
In the double-pushout approach to graph transformations, most authors assume the left-hand side to be injective, since the noninjective case leads to ambiguous results. Taking into...
Abstract. Social Network (SN) data has become ubiquitous, demanding advanced and flexible means to represent, transform and query such data. In addition to the intrinsic challenge...
We propose in this paper a novel approach to the induction of the structure of Hidden Markov Models. The induced model is seen as a lumped process of a Markov chain. It is construc...
Consider a scenario where one desires to simulate the execution of some graph algorithm on random input graphs of huge, perhaps even exponential size. Sampling and storing these h...