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...
Abstract. GP (for Graph Programs) is a rule-based, nondeterministic programming language for solving graph problems at a high level of abstraction, freeing programmers from handlin...
In this paper, we consider a large variety of solutions for the generation of Sierpinski triangles, one of the case studies for the AGTIVE graph transformation tool contest [15]. A...
Spatial relations play a crucial role in model-based image recognition and interpretation due to their stability compared to many other image appearance characteristics, and graphs...
The algorithm presented here, BCC, is an enhancement of the well known Backtrack used to solve constraint satisfaction problems. Though most backtrack improvements rely on propaga...