Directed network inference using a measure of directed information

The concept of mutual information (MI) has been widely used for inferring complex networks such as genetic regulatory networks. However, the MI based methods cannot infer directed or dynamic networks. In this paper, we propose a new network inference algorithm to infer directed acyclic networks which can determine both the connectivity and causality between different nodes based on the concept of directed information (DI) and conditional directed information. The proposed method is applied to both simulated data and Electroencephalography (EEG) data to evaluate its effectiveness.