Graph Convolutional Networks (GCNs) can capture non-Euclidean spatial dependence between different brain regions. The graph pooling operator, a crucial element of GCNs, enhances the representation learning capability and facilitates the acquisition of abnormal brain maps. However, most existing research designs graph pooling operators solely from the perspective of nodes while disregarding the original edge features. This confines graph pooling application scenarios and diminishes its ability to capture critical substructures. In this paper, we propose a novel edge-aware hard clustering graph pool (EHCPool), which is tailored to dominant edge features and redefines the clustering process. EHCPool initially introduced the 'Edge-to-Node' score criterion which utilized edge information to evaluate the significance of nodes. An innovative Iteration n-top strategy was then developed, guided by edge scores, to adaptively learn sparse hard clustering assignments for graphs. Additionally, a N-E Aggregation strategy is designed to aggregate node and edge features in each independent subgraph. Extensive experiments on the multi-site public datasets demonstrate the superiority and robustness of the proposed model. More notably, EHCPool has the potential to probe different types of dysfunctional brain networks from a data-driven perspective. Method code: https://github.com/swfen/EHCPool

翻译：图卷积网络（GCNs）能够捕捉不同脑区之间的非欧几里得空间依赖关系。作为GCNs的关键组成部分，图池化算子可增强表示学习能力，并有助于获取异常脑图谱。然而，现有研究大多仅从节点视角设计图池化算子，而忽略了原始边缘特征。这限制了图池化的应用场景，并削弱了其捕获关键子结构的能力。本文提出了一种新颖的边缘感知硬聚类图池化（EHCPool），该算子专为优势边缘特征设计，并重新定义了聚类过程。EHCPool首先引入"边到节点"评分准则，利用边缘信息评估节点的重要性；随后提出创新的迭代n-top策略，在边缘分数引导下自适应学习稀疏硬聚类分配。此外，还设计了N-E聚合策略，在每个独立子图中聚合节点与边缘特征。在多中心公开数据集上的大量实验证明，所提模型具有优越性和鲁棒性。更值得关注的是，EHCPool具备从数据驱动角度探测不同类型功能失调脑网络的潜力。方法代码：https://github.com/swfen/EHCPool