Graph Negative Feedback Bias Correction Framework for Adaptive Heterophily Modeling

2026-03-03

AI Summary

This work addresses the performance degradation of conventional graph neural networks (GNNs) on heterophilous graphs, a limitation rooted in their reliance on the homophily assumption. The authors propose a novel, aggregation-agnostic bias correction framework that incorporates a negative feedback mechanism: leveraging predictions from a graph-agnostic model as a feedback signal and guiding the learning process with Dirichlet energy to effectively suppress homophily bias induced by label autocorrelation. Requiring only minimal modifications to existing architectures, the method achieves substantial performance gains on heterophilous graphs without incurring significant computational or memory overhead. This approach demonstrates both broad applicability across mainstream GNNs and strong practical utility.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Graph Neural Networks (GNNs) have emerged as a powerful framework for processing graph-structured data. However, conventional GNNs and their variants are inherently limited by the homophily assumption, leading to degradation in performance on heterophilic graphs. Although substantial efforts have been made to mitigate this issue, they remain constrained by the message-passing paradigm, which is inherently rooted in homophily. In this paper, a detailed analysis of how the underlying label autocorrelation of the homophily assumption introduces bias into GNNs is presented. We innovatively leverage a negative feedback mechanism to correct the bias and propose Graph Negative Feedback Bias Correction (GNFBC), a simple yet effective framework that is independent of any specific aggregation strategy. Specifically, we introduce a negative feedback loss that penalizes the sensitivity of predictions to label autocorrelation. Furthermore, we incorporate the output of graph-agnostic models as a feedback term, leveraging independent node feature information to counteract correlation-induced bias guided by Dirichlet energy. GNFBC can be seamlessly integrated into existing GNN architectures, improving overall performance with comparable computational and memory overhead.

Problem

• heterophily
• graph neural networks
• homophily assumption
• label autocorrelation
• bias correction

Innovation

• Graph Neural Networks
• Heterophily
• Negative Feedback
• Bias Correction
• Label Autocorrelation