提出负反馈机制纠正图神经网络在异质图上的偏差，提升性能。
Graph Negative Feedback Bias Correction Framework for Adaptive Heterophily Modeling
- 用负反馈损失惩罚预测对标签自相关的敏感性，修正偏差。
- 引入无图模型输出作为反馈项，降低相关性导致的误差。
- 可无缝嵌入现有GNN架构，计算开销小，适合异质图场景。
图神经网络（GNN）在处理图结构数据方面表现强大，但传统GNN及其变体受同质性假设限制，在异质图上性能下降。本文分析了同质性假设中标签自相关如何引入偏差，并提出图负反馈偏差校正（GNFBC）框架。该框架通过负反馈损失惩罚预测对标签自相关的敏感性，同时引入图无关模型输出作为反馈项，利用独立节点特征信息，基于Dirichlet能量引导校正相关性偏差。GNFBC与聚合策略无关，可无缝集成到现有GNN架构中，显著提升性能，且计算与内存开销相当低。
原文摘要 · Abstract (English)
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.
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