AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification

Minjun Kim, Jaehyeon Choi, SeungJoo Lee, Jinhong Jung, U Kang

Abstract: How can we accurately classify graphs? Graph classification is a pivotal task in data mining with applications in social network analysis, web analysis, drug discovery, molecular property prediction, etc. Graph neural networks have achieved the state-of-the-art performance in graph classification, but they consistently struggle with overfitting. To mitigate overfitting, researchers introduced various representation learning methods utilizing graph augmentation. However, existing methods rely on simplistic use of graph augmentation, which loses augmentation-induced differences and limits the expressiveness of representations.