Directed Network Analysis Using Transfer Entropy Component Analysis

In this paper, we present a novel method for detecting directed network characteristics using histogram statistics based on degree distribution associated with transfer entropy. The proposed model in this paper established in information theory looks forward to learn the low dimensional representation of sample graphs, which can be obtained by transfer entropy component analysis (TECA) model. In particular, we apply transfer entropy to measure the transfer information between different time series data. For instances, for the fMRI time series data, we can use the transfer entropy to explore the connectivity between different brain functional regions effectively, which plays a significant role in diagnosing Alzheimers disease (AD) and its prodromal stage, mild cognitive impairment (MCI). With the properties of the directed graph in hand, we commence to further encode it into advanced representation of graphs based on the histogram statistics of degree distribution and multilinear principal component analysis (MPCA) technology. It not only reduces the memory space occupied by the huge transfer entropy matrix, but also enables the features to have a stronger representational capacity in the low-dimensional feature space. We conduct a classification experiment on the proposed model for the fMRI time series data. The experimental results verify that our model can significantly improve the diagnosis accuracy for MCI subjects.