Efficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderings


  • Niels Grüttemeier Philipps-Universität Marburg
  • Christian Komusiewicz Philipps-Universität Marburg
  • Nils Morawietz Philipps-Universität Marburg


Local Search, Bayesian Learning


In Bayesian Network Structure Learning (BNSL), we are given a variable set and parent scores for each variable and aim to compute a DAG, called Bayesian network, that maximizes the sum of parent scores, possibly under some structural constraints. Even very restricted special cases of BNSL are computationally hard, and, thus, in practice heuristics such as local search are used. In a typical local search algorithm, we are given some BNSL solution and ask whether there is a better solution within some pre-defined neighborhood of the solution. We study ordering-based local search, where a solution is described via a topological ordering of the variables. We show that given such a topological ordering, we can compute an optimal DAG whose ordering is within inversion distance r in subexponential FPT time; the parameter r allows to balance between solution quality and running time of the local search algorithm. This running time bound can be achieved for BNSL without any structural constraints and for all structural constraints that can be expressed via a sum of weights that are associated with each parent set. We show that for other modification operations on the variable orderings, algorithms with an FPT time for r are unlikely. We also outline the limits of ordering-based local search by showing that it cannot be used for common structural constraints on the moralized graph of the network.




How to Cite

Grüttemeier, N., Komusiewicz, C., & Morawietz, N. (2021). Efficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderings. Proceedings of the AAAI Conference on Artificial Intelligence, 35(14), 12328-12335. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17463



AAAI Technical Track on Search and Optimization