Structural network inference from time-series data using a generative model and transfer entropy

• Published in: Pattern recognition letters Vol. 125; pp. 357 - 363
• Main Authors: Zhang, Zhihong, Zhang, Genzhou, Zhang, Zhonghao, Chen, Guo, Zeng, Yangbin, Wang, Beizhan, Hancock, Edwin R.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2019
• Subjects: Expectation maximization algorithm; Network inference; Supergraph; Time series; Transfer entropy; Expectation maximization algorithm; Network inference; Time series; 41A10; 65D05; 65D17; Supergraph; Transfer entropy; 41A05
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2019.05.019

•We concentrate on the problem of describing the directed flow of information between nodes based on transfer entropy.•We have developed a weighted directed supergraph based on the von Neumann entropy of a directed graph.•Our model can improve the classification performance on fMRI brain connectivity data when the training data are limited. In this paper we develop a novel framework for inferring a generative model of network structure representing the causal relations between data for a set of objects characterized in terms of time series. To do this we make use of transfer entropy as a means of inferring directed information transfer between the time-series data. Transfer entropy allows us to infer directed edges representing the causal relations between pairs of time series, and has thus been used to infer directed graph representations of causal networks for time-series data. We use the expectation maximization algorithm to learn a generative model which captures variations in the causal network over time. We conduct experiments on fMRI brain connectivity data for subjects in different stages of the development of Alzheimer’s disease (AD). Here we use the technique to learn class exemplars for different stages in the development of the disease, together with a normal control class, and demonstrate its utility in both graph multi-class and binary classifications. These experiments are showing the effectiveness of our proposed framework when the amounts of training data are relatively small.