GTPool: Graph Transformer Pooling with Diverse Sampling

Graph pooling techniques have emerged as powerful tools for downsampling graphs, yielding impressive results on various graph-level tasks like graph classification and generation. Node dropping pooling stands out as a significant approach in this domain, utilizing learnable scoring functions to drop nodes with relatively lower significance. However, previous node dropping methods face two critical limitations: (1) they often struggle to capture long-range dependencies effectively for each pooled node, potentially limiting the expressiveness of node representations. (2) by exclusively retaining the highest-scoring nodes, they tend to preserve similar nodes, thereby discarding valuable information residing in low-scoring nodes; To address these issues, we propose a novel Graph Transformer Pooling method termed GTPool. GTPool constructs a node dropping pooling layer based on the Transformer architecture, aiming to efficiently capture long-range pairwise interactions while promoting diverse sampling. A key component of GTPool is its scoring module, grounded in the self-attention mechanism, which assesses the relevance between node representations and the graph representation. This nuanced approach offers a more natural means of measuring node importance. Additionally, GTPool adopts Roulette Wheel Sampling (RWS), a diversified sampling method that is capable of preserving nodes across various scoring intervals, rather than solely focusing on higher-scoring nodes. Consequently, GTPool can effectively capture long-range information and identify more representative nodes, thereby outperforming existing popular graph pooling methods across 14 benchmark datasets.