Imbalanced Graph Classification via Graph-of-Graph Neural Networks

Abstract

Graph Neural Networks (GNNs) have achieved unprecedented success in learning graph representations to identify categorical labels of graphs. However, most existing graph classification problems with GNNs follow a balanced data splitting protocol, which is misaligned with many real-world scenarios in which some classes have much fewer labels than others. Directly training GNNs under this imbalanced situation may lead to uninformative representations of graphs in minority classes, and compromise the overall performance of downstream classification, which signifies the importance of developing effective GNNs for handling imbalanced graph classification. Existing methods are either tailored for non-graph structured data or designed specifically for imbalance node classification while few focus on imbalance graph classification. To this end, we introduce a novel framework, Graph-of-Graph Neural Networks (G$^2$GNN), which alleviates the graph imbalance issue by deriving extra supervision globally from neighboring graphs and locally from graphs themselves. Globally, we construct a graph of graphs (GoG) based on kernel similarity and perform GoG propagation to aggregate neighboring graph representations, which are initially obtained by node-level propagation with pooling via a GNN encoder. Locally, we employ topological augmentation via masking nodes or dropping edges to improve the model generalizability in discerning topology of unseen testing graphs. Extensive graph classification experiments conducted on seven benchmark datasets demonstrate our proposed G$^2$GNN outperforms numerous baselines by roughly 5\% in both F1-macro and F1-micro scores. The implementation of G$^2$GNN is available at \href{https://github.com/YuWVandy/G2GNN}{https://github.com/YuWVandy/G2GNN}.