  Advanced graph neural networks have shown great potentials in graph
classification tasks recently. Different from node classification where node
embeddings aggregated from local neighbors can be directly used to learn node
labels, graph classification requires a hierarchical accumulation of different
levels of topological information to generate discriminative graph embeddings.
Still, how to fully explore graph structures and formulate an effective graph
classification pipeline remains rudimentary. In this paper, we propose a novel
graph neural network based on supervised contrastive learning with structure
inference for graph classification. First, we propose a data-driven graph
augmentation strategy that can discover additional connections to enhance the
existing edge set. Concretely, we resort to a structure inference stage based
on diffusion cascades to recover possible connections with high node
similarities. Second, to improve the contrastive power of graph neural
networks, we propose to use a supervised contrastive loss for graph
classification. With the integration of label information, the one-vs-many
contrastive learning can be extended to a many-vs-many setting, so that the
graph-level embeddings with higher topological similarities will be pulled
closer. The supervised contrastive loss and structure inference can be
naturally incorporated within the hierarchical graph neural networks where the
topological patterns can be fully explored to produce discriminative graph
embeddings. Experiment results show the effectiveness of the proposed method
compared with recent state-of-the-art methods.
