Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown promising performance for representation learning on graphs, which train models by maximizing agreement between original graphs and their augmented views (i.e., positive views). Unfortunately, these methods usually involve pre-defined augmentation strategies based on the knowledge of human experts. Moreover, these strategies may fail to generate challenging positive views to provide sufficient supervision signals. In this paper, we present a novel approach named Graph Pooling ContraSt (GPS) to address these issues. Motivated by the fact that graph pooling can adaptively coarsen the graph with the removal of redundancy, we rethink graph pooling and leverage it to automatically generate multi-scale positive views with varying emphasis on providing challenging positives and preserving semantics, i.e., strongly-augmented view and weakly-augmented view. Then, we incorporate both views into a joint contrastive learning framework with similarity learning and consistency learning, where our pooling module is adversarially trained with respect to the encoder for adversarial robustness. Experiments on twelve datasets on both graph classification and transfer learning tasks verify the superiority of the proposed method over its counterparts.

自我监督图表示学习是最近在生物信息学和社交网络等领域显示出相当潜力的技术。本文介绍了一种名为Graph Pooling ContraSt（GPS）的新方法，通过重新思考图池化（graph pooling）并利用其自动生成多尺度的正视图，从而在相似性学习和一致性学习的共同对比学习框架中将强增强视图和弱增强视图结合起来，并对编码器进行对抗训练以提高其对抗鲁棒性。实验结果验证了该方法在图分类和迁移学习任务中的优越性。

GPS: 基于对抗汇聚的多尺度增强视图的图对比学习