Exact structure learning of Bayesian networks by optimal path extension

Bayesian networks are probabilistic graphical models often used in big data analytics. The problem of Bayesian network exact structure learning is to find a network structure that is optimal under certain scoring criteria. The problem is known to be NP-hard and the existing methods are both computationally and memory intensive. In this paper, we introduce a new approach for exact structure learning that leverages relationship between a partial network structure and the remaining variables to constrain the number of ways in which the partial network can be optimally extended. Via experimental results, we show that the method provides up to three times improvement in runtime, and orders of magnitude reduction in memory consumption over the current best algorithms.