Due to the homophily assumption in graph convolution networks (GNNs), a common consensus in the graph node classification task is that GNNs perform well on homophilic graphs but may fail on heterophilic graphs with many inter-class edges. However, the previous inter-class edges perspective and related homo-ratio metrics cannot well explain the GNNs performance under some heterophilic datasets, which implies that not all the inter-class edges are harmful to GNNs. In this work, we propose a new metric based on von Neumann entropy to re-examine the heterophily problem of GNNs and investigate the feature aggregation of inter-class edges from an entire neighbor identifiable perspective. Moreover, we propose a simple yet effective Conv-Agnostic GNN framework (CAGNNs) to enhance the performance of most GNNs on heterophily datasets by learning the neighbor effect for each node. Specifically, we first decouple the feature of each node into the discriminative feature for downstream tasks and the aggregation feature for graph convolution. Then, we propose a shared mixer module to adaptively evaluate the neighbor effect of each node to incorporate the neighbor information. The proposed framework can be regarded as a plug-in component and is compatible with most GNNs. The experimental results over nine well-known benchmark datasets indicate that our framework can significantly improve performance, especially for the heterophily graphs. The average performance gain is 9.81%, 25.81%, and 20.61% compared with GIN, GAT, and GCN, respectively. Extensive ablation studies and robustness analysis further verify the effectiveness, robustness, and interpretability of our framework. Code is available at https://github.com/JC-202/CAGNN.

翻译：由于图卷积网络（GNNs）中的同配假设，图节点分类任务中普遍共识是：GNNs在同配图上表现良好，但在包含大量跨类别边的异配图上可能失效。然而，以往基于跨类别边的视角及相关的同配率指标无法充分解释某些异配数据集下GNNs的性能表现，这表明并非所有跨类别边都会对GNNs造成损害。本文提出一种基于冯·诺依曼熵的新指标，重新审视GNNs的异配问题，并从整体邻居可识别性角度研究跨类别边的特征聚合机制。此外，我们提出一个简单而有效的卷积无关GNN框架（CAGNNs），通过学习每个节点的邻居效应来增强大多数GNNs在异配数据集上的性能。具体而言，我们首先将每个节点的特征解耦为面向下游任务的判别性特征和用于图卷积的聚合特征。随后提出共享混合器模块，自适应评估每个节点的邻居效应以整合邻居信息。该框架可作为即插即用组件，兼容大多数GNNs。在九个知名基准数据集上的实验表明，我们的框架能显著提升性能，特别是在异配图上。相较于GIN、GAT和GCN，平均性能增益分别达到9.81%、25.81%和20.61%。大量消融实验和鲁棒性分析进一步验证了框架的有效性、鲁棒性和可解释性。代码已开源：https://github.com/JC-202/CAGNN。