MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks

Part of International Conference on Representation Learning 2025 (ICLR 2025) Conference

Authors

Carlo Abate, Filippo Maria Bianchi

Abstract

We propose a novel approach to compute the MAXCUT in attributed graphs, i.e., graphs with features associated with nodes and edges. Our approach works well on any kind of graph topology and can find solutions that jointly optimize the MAXCUT along with other objectives. Based on the obtained MAXCUT partition, we implement a hierarchical graph pooling layer for Graph Neural Networks, which is sparse, trainable end-to-end, and particularly suitable for downstream tasks on heterophilic graphs.