Year: 2024
Type: article
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. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
Institution: University of California, Irvine
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: Neural basis of the memory for sequences of events: A synergistic approach in rats and humans, G2318624699, Collaborative Research: Integrative Heterogeneous Learning for Intensive Complex Longitudinal Data, G3658124931, G4300989825, G6671297155, G6791967163, NSF-Simons Center for Multiscale Cell Fate Research, Collaborative Research: NCS-FR: DEJA-VU: Design of Joint 3D Solid-State Learning Machines for Various Cognitive Use-Cases, Nicotinic enhancement of auditory-cognitive processing, CAREER: Hippocampal and prefrontal mechanisms underlying the temporal context of episodic memory, Scalable Bayesian Stochastic Process Models for Neural Data Analysis, G986754019
OpenAlex ID: W4402665318