We introduce BN-Pool, the first clustering-based pooling method for Graph Neural Networks that adaptively determines the number of supernodes in a coarsened graph. BN-Pool leverages a generative model based on a Bayesian nonparametric framework for partitioning graph nodes into an unbounded number of clusters. During training, the node-to-cluster assignments are learned by combining the supervised loss of the downstream task with an unsupervised auxiliary term, which encourages the reconstruction of the original graph topology while penalizing unnecessary proliferation of clusters. By automatically discovering the optimal coarsening level for each graph, BN-Pool preserves the performance of soft-clustering pooling methods while avoiding their typical redundancy by learning compact pooled graphs. The code is available at https://github.com/NGMLGroup/Bayesian-Nonparametric-Graph-Pooling.

翻译：我们提出了BN-Pool，这是首个基于聚类的图神经网络池化方法，能够自适应地确定粗化图中超节点的数量。BN-Pool利用基于贝叶斯非参数框架的生成模型，将图节点划分到无界数量的聚类中。训练过程中，节点到聚类的分配通过结合下游任务的监督损失与无监督辅助项来学习，其中辅助项鼓励对原始图拓扑结构进行重构，同时惩罚不必要的聚类增殖。通过自动发现每个图的最优粗化水平，BN-Pool在保持软聚类池化方法性能的同时，通过学习紧凑的池化图避免了其典型的冗余性。代码可在https://github.com/NGMLGroup/Bayesian-Nonparametric-Graph-Pooling获取。