Bounding Graphical Models Processing by Hypertree Width

In 2000, Gottlob et al. [3] introduced a new graph parameter, the hypertree width, and showed that it provides a broader characterization of tractable constraint networks than the treewidth. In 2005 this observation was extended to general graphical models [5], showing that the hypertree width yields bounds on inference algorithms. This paper explores further the practical properties of the hypertree width parameter for bounding the complexity of constraint satisfaction and optimization tasks. To that end we study empirically the effectiveness of the treewidth vs. hypertree width over common network benchmarks.