Graph neural networks (GNNs) have recently been popular in natural language and programming language processing, particularly in text and source code classification. Graph pooling which processes node representation into the entire graph representation, which can be used for multiple downstream tasks, e.g., graph classification, is a crucial component of GNNs. Recently, to enhance graph learning, Manifold Mixup, a data augmentation strategy that mixes the graph data vector after the pooling layer, has been introduced. However, since there are a series of graph pooling methods, how they affect the effectiveness of such a Mixup approach is unclear. In this paper, we take the first step to explore the influence of graph pooling methods on the effectiveness of the Mixup-based data augmentation approach. Specifically, 9 types of hybrid pooling methods are considered in the study, e.g., $\mathcal{M}_{sum}(\mathcal{P}_{att},\mathcal{P}_{max})$. The experimental results on both natural language datasets (Gossipcop, Politifact) and programming language datasets (Java250, Python800) demonstrate that hybrid pooling methods are more suitable for Mixup than the standard max pooling and the state-of-the-art graph multiset transformer (GMT) pooling, in terms of metric accuracy and robustness.

本文探讨了不同的图池化方法对基于混合数据增广方法（Manifold Mixup）的影响，并在自然语言和编程语言数据集上进行了实验，结果表明混合池化方法比标准的最大池化和最先进的图多重集变换器（GMT）池化更适合于Mixup，具有更好的度量精度和鲁棒性。

混合池化增强基于Mixup的图学习在自然语言处理中的应用