  Recently, methods that represent data as a graph, such as graph neural
networks (GNNs) have been successfully used to learn data representations and
structures to solve classification and link prediction problems. The
applications of such methods are vast and diverse, but most of the current work
relies on the assumption of a static graph. This assumption does not hold for
many highly dynamic systems, where the underlying connectivity structure is
non-stationary and is mostly unobserved. Using a static model in these
situations may result in sub-optimal performance. In contrast, modeling changes
in graph structure with time can provide information about the system whose
applications go beyond classification. Most work of this type does not learn
effective connectivity and focuses on cross-correlation between nodes to
generate undirected graphs. An undirected graph is unable to capture direction
of an interaction which is vital in many fields, including neuroscience. To
bridge this gap, we developed dynamic effective connectivity estimation via
neural network training (DECENNT), a novel model to learn an interpretable
directed and dynamic graph induced by the downstream classification/prediction
task. DECENNT outperforms state-of-the-art (SOTA) methods on five different
tasks and infers interpretable task-specific dynamic graphs. The dynamic graphs
inferred from functional neuroimaging data align well with the existing
literature and provide additional information. Additionally, the temporal
attention module of DECENNT identifies time-intervals crucial for predictive
downstream task from multivariate time series data.
