提出可微的图聚类方法，用于提升异质图神经网络性能
MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks
- 基于节点边特征设计可微分最大割算法
- 实现稀疏可训练的层次化图池化层
- 特别适合异质图下游任务
我们提出一种新的属性图最大割计算方法，即带有节点和边特征的图。该方法适用于任意图拓扑结构，并能联合优化最大割与其他目标。基于所得最大割划分结果，我们构建了一种层次化图池化层，具有稀疏性、端到端可训练性，特别适用于异质图的下游任务。
原文摘要 · Abstract (English)
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.
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