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。图池化算子作为GCNs的关键组成部分，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角度设计图池化算子，忽视了原始边特征。这限制了图池化的应用场景，削弱了其捕获关键子结构的能力。本文提出一种新颖的边缘感知硬聚类图池（EHCPool），该算子专为突出边特征设计并重构了聚类过程。EHCPool首先引入了"边到节点"评分准则，利用边信息评估节点重要性；随后基于边分数创新性地开发了迭代n-top策略，以自适应学习图的稀疏硬聚类分配；并设计了N-E聚合策略，在每个独立子图中聚合节点与边特征。在多站点公开数据集上的大量实验证明了所提模型的优越性与鲁棒性。尤为值得注意的是，EHCPool具有从数据驱动角度探测不同类型脑功能网络异常的潜力。方法代码：https://github.com/swfen/EHCPool