  Graph classification is a challenging research problem in many applications
across a broad range of domains. In these applications, it is very common that
class distribution is imbalanced. Recently, Graph Neural Network (GNN) models
have achieved superior performance on various real-world datasets. Despite
their success, most of current GNN models largely overlook the important
setting of imbalanced class distribution, which typically results in prediction
bias towards majority classes. To alleviate the prediction bias, we propose to
leverage semantic structure of dataset based on the distribution of node
embedding. Specifically, we present GraphDIVE, a general framework leveraging
mixture of diverse experts (i.e., graph classifiers) for imbalanced graph
classification. With a divide-and-conquer principle, GraphDIVE employs a gating
network to partition an imbalanced graph dataset into several subsets. Then
each expert network is trained based on its corresponding subset. Experiments
on real-world imbalanced graph datasets demonstrate the effectiveness of
GraphDIVE.
