Graph pooling methods have been widely used on downsampling graphs, achieving
impressive results on multiple graph-level tasks like graph classification and
graph generation. An important line called node dropping pooling aims at
exploiting learnable scoring functions to drop nodes with comparatively lower
significance scores. However, existing node dropping methods suffer from two
limitations: (1) for each pooled node, these models struggle to capture
long-range dependencies since they mainly take GNNs as the backbones; (2)
pooling only the highest-scoring nodes tends to preserve similar nodes, thus
discarding the affluent information of low-scoring nodes. To address these
issues, we propose a Graph Transformer Pooling method termed GTPool, which
introduces Transformer to node dropping pooling to efficiently capture
long-range pairwise interactions and meanwhile sample nodes diversely.
Specifically, we design a scoring module based on the self-attention mechanism
that takes both global context and local context into consideration, measuring
the importance of nodes more comprehensively. GTPool further utilizes a
diversified sampling method named Roulette Wheel Sampling (RWS) that is able to
flexibly preserve nodes across different scoring intervals instead of only
higher scoring nodes. In this way, GTPool could effectively obtain long-range
information and select more representative nodes. Extensive experiments on 11
benchmark datasets demonstrate the superiority of GTPool over existing popular
graph pooling methods.

我们提出了一种名为 GTPool 的图形变换汇聚方法，它引入了变换器来进行节点丢弃汇聚，以有效地捕获长程成对交互并同时进行多样化节点抽样。与现有方法相比，GTPool 在 11 个基准数据集上的广泛实验证明了其优越性。