MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks
Keywords: Graph neural networks, graph pooling, graph coarsening, maxcut
TL;DR: A GNN-based approach for computing MAXCUT on attributed graphs, used to implement graph pooling.
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.
Primary Area: learning on graphs and other geometries & topologies
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Submission Number: 337
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