GNN Backbone

A GNN backbone refers to the core architecture of a Graph Neural Network (GNN), providing the fundamental framework for processing graph-structured data. Current research focuses on improving GNN backbones to address challenges like heterophily (nodes with dissimilar neighbors), data scarcity, and robustness to noisy data, often employing techniques such as adversarial training, uncertainty estimation, and personalized message passing. These advancements aim to enhance GNN performance across various tasks, including node classification, link prediction, and graph-level classification, leading to more accurate, reliable, and explainable models for diverse applications.