  Graph Neural Networks (GNNs) have enjoyed wide spread applications in
graph-structured data. However, existing graph based applications commonly lack
annotated data. GNNs are required to learn latent patterns from a limited
amount of training data to perform inferences on a vast amount of test data.
The increased complexity of GNNs, as well as a single point of model parameter
initialization, usually lead to overfitting and sub-optimal performance. In
addition, it is known that GNNs are vulnerable to adversarial attacks. In this
paper, we push one step forward on the ensemble learning of GNNs with improved
accuracy, generalization, and adversarial robustness. Following the principles
of stochastic modeling, we propose a new method called GNN-Ensemble to
construct an ensemble of random decision graph neural networks whose capacity
can be arbitrarily expanded for improvement in performance. The essence of the
method is to build multiple GNNs in randomly selected substructures in the
topological space and subfeatures in the feature space, and then combine them
for final decision making. These GNNs in different substructure and subfeature
spaces generalize their classification in complementary ways. Consequently,
their combined classification performance can be improved and overfitting on
the training data can be effectively reduced. In the meantime, we show that
GNN-Ensemble can significantly improve the adversarial robustness against
attacks on GNNs.
