Graph Negative Feedback Bias Correction Framework for Adaptive Heterophily Modeling

Researchers propose Graph Negative Feedback Bias Correction (GNFBC), a framework that addresses limitations in Graph Neural Networks when processing heterophilic graphs where connected nodes have different characteristics. The method uses negative feedback mechanisms to correct bias from homophily assumptions and can be integrated into existing GNN architectures with minimal computational overhead.