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Graph Negative Feedback Bias Correction Framework for Adaptive Heterophily Modeling

Jiaqi Lv, Qingfeng Du, Yu Zhang, Yongqi Han, Sheng Li

TL;DR

A negative feedback loss is introduced that penalizes the sensitivity of predictions to label autocorrelation and incorporates the output of graph-agnostic models as a feedback term, leveraging independent node feature information to counteract correlation-induced bias guided by Dirichlet energy.

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.

Graph Negative Feedback Bias Correction Framework for Adaptive Heterophily Modeling

TL;DR

A negative feedback loss is introduced that penalizes the sensitivity of predictions to label autocorrelation and incorporates the output of graph-agnostic models as a feedback term, leveraging independent node feature information to counteract correlation-induced bias guided by Dirichlet energy.

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.
Paper Structure (31 sections, 24 equations, 5 figures, 4 tables, 1 algorithm)

This paper contains 31 sections, 24 equations, 5 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminaries
  4. Homophily and Heterophily
  5. Negative Feedback
  6. Graph-aware and Graph-agnostic Models
  7. Theoretical Analysis
  8. Methodology
  9. Negative Feedback Loss
  10. Graph-Agnostic Model Feedback Correction
  11. Dirichlet Energy-Based Feedback Coefficient
  12. Inference Process
  13. Experiments
  14. Datasets
  15. Experimental Setup
  16. ...and 16 more sections

Figures (5)

  • Figure 1: Schematic diagram of the negative feedback system and its adjustment process toward a new steady state. (a) denotes the training process, and (b) denotes the inference process.
  • Figure 2: GNFBC employs a graph-agnostic model to perform negative feedback correction during training. The graph-aware model and the graph-agnostic model share features and weights. During the inference stage, GNFBC follows the standard inference process.
  • Figure 3: Performance comparison between GNFBC and standalone Graph-aware or Graph-agnostic model on homophilic and heterophilic datasets.
  • Figure 4: Performance of SGC, GCN, and GAT models with the GNFBC framework on the YelpChi dataset
  • Figure 5: Performance of SGC, GCN, and GAT models with the GNFBC framework on the Texas dataset