Random Generation of Bayesian Networks
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Density Functions for Visual Attributes and Effective Partitioning in Graph Visualization
INFOVIS '00 Proceedings of the IEEE Symposium on Information Vizualization 2000
On inclusion-driven learning of bayesian networks
The Journal of Machine Learning Research
Structure learning with independent non-identically distributed data
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Programming support and scheduling for communicating parallel tasks
Journal of Parallel and Distributed Computing
Bayesian probabilities for constraint-based causal discovery
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The Graph Drawing community uses test suites for comparing layout quality and efficiency. Those suites often claim to collect randomly generated graphs, although in most cases randomness is a loosely defined notion. We propose a simple algorithm for generating acyclic digraphs with a given number of vertices uniformly at random. Applying standard combinatorial techniques, we describe the overall shape and average edge density of an acyclic digraph. The usefulness of our algorithm resides in the possibility of controlling edge density of the generated graphs. We have used our technique to build a large test suite of acyclic digraphs with various edge density and number of vertices ranging from 10 to 1000.