PassAugment: Pass Nodes Importance in Graph Data Augmentation for Graph Classification

Data augmentation has been widely introduced into graph-based tasks to improve the generalizability of models. Based on empirical hypothesis, we show that the node in a graph has different importance, the important one is critical for classification task while the unimportant one hurts the performance. However, there are few works in data augmentation addressing the information propagation of nodes with different importance. In this work, we propose a novel graph data augmentation algorithm for graph classification task, called PassAugment, aiming to pass these importance in graph data augmentation. After distinguishing the importance of all nodes in each graph using the saliency map, we design a data augmentation approach including two strategies: (i) randomly adding edges between the important nodes and the other nodes to globally improve the effective information passing, and (ii) randomly removing edges between the unimportant nodes and their neighbors to locally reduce the ineffective information passing. More importantly, our proposed approach as a standalone module can be combined with many GNNs architectures. Experimental results on graph classification task show that our approach consistently improves the accuracy and achieves or closely matches the state-of-the-art performance.