  Recent advances in graph convolutional networks have significantly improved
the performance of chemical predictions, raising a new research question: "how
do we explain the predictions of graph convolutional networks?" A possible
approach to answer this question is to visualize evidence substructures
responsible for the predictions. For chemical property prediction tasks, the
sample size of the training data is often small and/or a label imbalance
problem occurs, where a few samples belong to a single class and the majority
of samples belong to the other classes. This can lead to uncertainty related to
the learned parameters of the machine learning model. To address this
uncertainty, we propose BayesGrad, utilizing the Bayesian predictive
distribution, to define the importance of each node in an input graph, which is
computed efficiently using the dropout technique. We demonstrate that BayesGrad
successfully visualizes the substructures responsible for the label prediction
in the artificial experiment, even when the sample size is small. Furthermore,
we use a real dataset to evaluate the effectiveness of the visualization. The
basic idea of BayesGrad is not limited to graph-structured data and can be
applied to other data types.
