  Graph neural network (GNN)-based graph learning has been popular in natural
language and programming language processing, particularly in text and source
code classification. Typically, GNNs are constructed by incorporating
alternating layers which learn transformations of graph node features, along
with graph pooling layers that use graph pooling operators (e.g., Max-pooling)
to effectively reduce the number of nodes while preserving the semantic
information of the graph. Recently, to enhance GNNs in graph learning tasks,
Manifold-Mixup, a data augmentation technique that produces synthetic graph
data by linearly mixing a pair of graph data and their labels, has been widely
adopted. However, the performance of Manifold-Mixup can be highly affected by
graph pooling operators, and there have not been many studies that are
dedicated to uncovering such affection. To bridge this gap, we take an early
step to explore how graph pooling operators affect the performance of
Mixup-based graph learning. To that end, we conduct a comprehensive empirical
study by applying Manifold-Mixup to a formal characterization of graph pooling
based on 11 graph pooling operations (9 hybrid pooling operators, 2 non-hybrid
pooling operators). The experimental results on both natural language datasets
(Gossipcop, Politifact) and programming language datasets (JAVA250, Python800)
demonstrate that hybrid pooling operators are more effective for Manifold-Mixup
than the standard Max-pooling and the state-of-the-art graph multiset
transformer (GMT) pooling, in terms of producing more accurate and robust GNN
models.
