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 10
well-known GNN backbones on 9 benchmark datasets validate the superiority and
effectiveness of CARE over its GNN counterparts.

本文提出了一种 Class-Aware Representation refinement (CARE) 的方法，通过计算一个简单而强大的类别表示，并注入到图的表示中来提高类别可分离性，从而解决了图神经网络（GNN）在图分类任务中对于图层级关系和泛化问题的缺陷，并在多项实验中验证了其有效性。