  Graph Neural Networks (GNNs) are widely used for graph representation
learning. Despite its prevalence, GNN suffers from two drawbacks in the graph
classification task, the neglect of graph-level relationships, and the
generalization issue. Each graph is treated separately in GNN message
passing/graph pooling, and existing methods to address overfitting operate on
each individual graph. This makes the graph representations learnt less
effective in the downstream classification. In this paper, we propose a
Class-Aware Representation rEfinement (CARE) framework for the task of graph
classification. CARE computes simple yet powerful class representations and
injects them to steer the learning of graph representations towards better
class separability. CARE is a plug-and-play framework that is highly flexible
and able to incorporate arbitrary GNN backbones without significantly
increasing the computational cost. We also theoretically prove that CARE has a
better generalization upper bound than its GNN backbone through
Vapnik-Chervonenkis (VC) dimension analysis. Our extensive experiments with 11
well-known GNN backbones on 9 benchmark datasets validate the superiority and
effectiveness of CARE over its GNN counterparts.
