Graph Convolutional Networks (GCNs) can capture non-Euclidean spatial dependence between different brain regions, and the graph pooling operator in GCNs is key to enhancing the representation learning capability and acquiring abnormal brain maps. However, the majority of existing research designs graph pooling operators only 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. In this study, a clustering graph pooling method that first supports multidimensional edge features, called Edge-aware hard clustering graph pooling (EHCPool), is developed. EHCPool proposes the first 'Edge-to-node' score evaluation criterion based on edge features to assess node feature significance. To more effectively capture the critical subgraphs, a novel Iteration n-top strategy is further designed to adaptively learn sparse hard clustering assignments for graphs. Subsequently, an innovative N-E Aggregation strategy is presented to aggregate node and edge feature information in each independent subgraph. The proposed model was evaluated on multi-site brain imaging public datasets and yielded state-of-the-art performance. We believe this method is the first deep learning tool with the potential to probe different types of abnormal functional brain networks from 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，而GCNs中的图池化算子对于提升表征学习能力及获取异常脑图谱至关重要。然而，现有大部分研究仅从节点角度设计图池化算子，忽视了原始边特征，这不仅限制了图池化的应用场景，也削弱了其捕捉关键子结构的能力。本研究开发了一种首个支持多维边特征的聚类图池化方法——边感知硬聚类图池化（EHCPool）。EHCPool提出了首个基于边特征的"边到节点"评分评估准则，用于评估节点特征重要性。为更有效地捕捉关键子图，进一步设计了一种新颖的迭代n-top策略，自适应地学习图的稀疏硬聚类分配。随后，提出了一种创新的N-E聚合策略，用于聚合每个独立子图中的节点与边特征信息。该模型在多站点脑影像公开数据集上进行了评估，并取得了最先进的性能。我们相信，该方法是首个具备从数据驱动视角探测不同类型异常功能脑网络潜力的深度学习工具。核心代码见：https://github.com/swfen/EHCPool。