Labeled DBN Learning with Community Structure Knowledge

Abstract

Learning interactions between dynamical processes is a widespread but difficult problem in ecological or human sciences. Unlike in other domains (bioinformatics, for example), data is often scarce, but expert knowledge is available. We consider the case where knowledge is about a limited number of interactions that drive the processes dynamics, and on a community structure in the interaction network. We propose an original framework, based on Dynamic Bayesian Networks with labeled-edge structure and parsimonious parameterization, and a Stochastic Block Model prior, to integrate this knowledge. Then we propose a restoration-estimation algorithm, based on 0-1 Linear Programing, that improves network learning when these two types of expert knowledge are available. The approach is illustrated on a problem of ecological interaction network learning.