e Computação, Universidade de São Paulo, São Carlos, Brazil

Department of Mathematics, Imperial College London, London SW7 2AZ, UK

Department of Bioinformatics and Genetics, Kadir Has University, 34083 Istanbul, Turkey

Department of Mathematics and Statistics, University of Victoria, Victoria BC, CA

We are surrounded by a range of complex networks consisting of a large number of intricately coupled nodes. Neuron networks form an important class examples where the interaction structure is heterogeneous [@kandel2000]. Because changes in the interaction can have massive ramifications on the system as a whole, it is desirable to predict such disturbances and thus enact precautionary measures to avert potential disasters. For instance, neurological disorders such as Parkinson’s disease, schizophrenia, and epilepsy, are thought to be associated with anomalous interaction structure among neurons [@bohland2009]. Often, as in the case of neuron networks, it is impossible to directly determine the interaction structure. Therefore, a major scientific challenge is to develop techniques which use measurements of the time evolution of the nodes state to indirectly recover the network structure and predict the network’s behaviour when essential characteristics of the interactions change.

The literature on data-based network reconstruction is vast. Reconstruction methods can be classified into [*model-free*]{} methods and *model-based* 