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.

翻译：图卷积网络能够捕捉不同脑区间的非欧几里德空间依赖关系，其中图池化算子对增强表征学习能力及获取异常脑图谱具有关键作用。然而，现有研究大多仅从节点视角设计图池化算子，而忽略了原始边特征，这不仅限制了图池化的应用场景，还削弱了其对关键子结构的捕捉能力。本研究提出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基于边特征首创了"边到节点"评分评估准则，用于评估节点特征重要性。为更有效捕捉关键子图，进一步设计了新型迭代n-top策略，以自适应学习图的稀疏硬聚类分配。随后，创新性地提出N-E聚合策略，用于在每个独立子图中聚合节点与边的特征信息。该模型在多中心脑影像公开数据集上进行了评估，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角度探测不同类型异常功能脑网络潜力的深度学习工具。