A Class-Aware Representation Refinement Framework for Graph Classification

TL;DR

This paper introduces CARE, a flexible framework that enhances graph classification by incorporating class-aware representations, improving generalization and class separability without significant computational overhead.

Contribution

CARE is a novel, plug-and-play framework that injects class representations into GNNs, improving graph classification performance and generalization bounds.

Findings

CARE outperforms baseline GNNs on benchmark datasets.

Theoretical analysis shows better generalization bounds for CARE.

Extensive experiments validate CARE's effectiveness across multiple GNN backbones.

Abstract

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.