A Model-Agnostic Graph Neural Network for Integrating Local and Global Information

TL;DR

This paper introduces MaGNet, a model-agnostic GNN framework that integrates multi-order information, enhances interpretability, and demonstrates superior performance in simulations and real-world brain data analysis.

Contribution

MaGNet is a novel GNN framework that effectively combines information of various orders and provides interpretable results, addressing key limitations of existing GNNs.

Findings

MaGNet outperforms state-of-the-art GNNs in simulated data.

MaGNet successfully extracts critical information from brain activity data.

Theoretical generalization bounds are established for MaGNet.

Abstract

Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, however, existing GNNs suffer from two significant limitations: a lack of interpretability in their 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 effectively 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 node features. Theoretically, we establish the generalization error bound for MaGNet via empirical Rademacher complexity, and demonstrate 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.