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, in a way that not only confines graph pooling application scenarios, but also diminishes its ability to capture critical substructures. To design a graph clustering pooling operator that is tailored to dominant edge features, we proposed the edge-aware hard clustering graph pool (EHCPool) and redefined the graph clustering process. Specifically, the 'Edge-to-node' criterion was proposed to evaluate the significance of both edge and node features. Guided by edge scores, we designed a revolutionary Iteration n-top strategy, aimed at adaptively learning sparse hard clustering assignments for graphs. Subsequently, a novel N-E Aggregation strategy is introduced to aggregate node and edge information 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. Core code is at: 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性。作为GCNs的关键组成部分，图池化算子增强了表示学习能力，并有助于获取异常脑图谱。然而，现有大多数研究仅从节点角度设计图池化算子，忽略了原始的边特征，这不仅限制了图池化的应用场景，也削弱了其捕获关键子结构的能力。为设计一种适配主导边特征的图聚类池化算子，我们提出了面向边的硬聚类图池化（EHCPool）方法，并重新定义了图聚类过程。具体而言，我们提出了"边到节点"准则以评估边特征与节点特征的重要性。在边分数的引导下，我们设计了一种革命性的迭代n-top策略，旨在自适应地学习图的稀疏硬聚类分配。随后，引入了一种新颖的N-E聚合策略，以在每个独立子图中聚合节点与边的信息。在多站点公共数据集上的大量实验表明，所提模型具有优越性和鲁棒性。更值得注意的是，EHCPool具备从数据驱动角度探查不同类型脑功能网络异常的能力。核心代码见：https://github.com/swfen/EHCPool。