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

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

SUBMITTER: Zhou W 

PROVIDER: S-EPMC12333528 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature