Graph Neural Networks (GNNs) have achieved promising performance in a variety
of graph-focused tasks. Despite their success, existing GNNs suffer from two
significant limitations: a lack of interpretability in results due to their
black-box nature, and an inability to learn representations of varying orders.
To tackle these issues, we propose a novel Model-agnostic Graph Neural Network
(MaGNet) framework, which is able to sequentially integrate information of
various orders, extract knowledge from high-order neighbors, and provide
meaningful and interpretable results by identifying influential compact graph
structures. In particular, MaGNet consists of two components: an estimation
model for the latent representation of complex relationships under graph
topology, and an interpretation model that identifies influential nodes, edges,
and important node features. Theoretically, we establish the generalization
error bound for MaGNet via empirical Rademacher complexity, and showcase its
power to represent layer-wise neighborhood mixing. We conduct comprehensive
numerical studies using simulated data to demonstrate the superior performance
of MaGNet in comparison to several state-of-the-art alternatives. Furthermore,
we apply MaGNet to a real-world case study aimed at extracting task-critical
information from brain activity data, thereby highlighting its effectiveness in
advancing scientific research.

我们提出了一个新的模型无关的图神经网络（MaGNet）框架，能够顺序地集成不同顺序的信息，从高阶邻居中提取知识，并通过识别有影响力的紧凑图结构来提供有意义、可解释的结果。理论上，我们通过经验 Rademacher 复杂性建立了 MaGNet 的泛化误差界，并展示了它在表示逐层邻域混合方面的能力。使用模拟数据进行了全面的数值研究，证明了 MaGNet 相对于几种最先进的替代方法的卓越性能。此外，我们将 MaGNet 应用于一个从脑活动数据中提取关键信息的实际案例研究，从而突显了它在推动科学研究方面的有效性。