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The analysis of multiple time series data, which are generated from a networked system, has attracted much attention recently. This technique has been used in a wide range of applications including functional brain network analysis of neuroimaging data and social influence analysis. In functional brain network analysis, the activity of different brain regions can be represented as multiple time series. An important task in the analysis is to identify the latent network from the observed time series data. In this network, the edges (functional connectivity) capture the correlation between different time series (brain regions). Conventional network extraction approaches usually focus on capturing the connectivity through linear measures under unsupervised settings. In this paper, we study the problem of identifying deep nonlinear connections under group-contrasting settings, where we have two groups of time series samples, and the goal is to identify nonlinear connections that are discriminative across the two groups. We propose a method called GCC (Graph Construction CNN) which is based on deep convolutional neural networks for the task of network construction. The CNN in our model learns a nonlinear edge-weighting function to assign discriminative values to the edges of a network. Experiments on a real-world ADHD dataset show that our proposed method can effectively identify the nonlinear connections among different brain regions. We also demonstrate the extensibility of our proposed framework by combining it with an autoencoder to capture subgraph patterns from the constructed networks.

Identifying Deep Contrasting Networks from Time Series Data: Application to Brain Network Analysis