---
title: "Adversarial Training for Graph Convolutional Networks: Stability and Generalization Analysis"
authors:
  - "Chang Cao"
  - "Han Li"
  - "Yulong Wang"
  - "Rui Wu"
  - "Hong Chen"
doi: "10.24963/ijcai.2025/534"
url: "https://ijcai.org/proceedings/2025/534"
pdf: "https://ijcai.org/proceedings/2025/0534.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "4797-4805"
track: "Main Track"
keywords:
  - "Machine Learning: ML: Sequence and graph learning"
  - "Machine Learning: ML: Adversarial machine learning"
  - "Machine Learning: ML: Learning theory"
---

# Adversarial Training for Graph Convolutional Networks: Stability and Generalization Analysis

Chang Cao, Han Li, Yulong Wang, Rui Wu, Hong Chen

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 4797-4805. DOI: [10.24963/ijcai.2025/534](https://doi.org/10.24963/ijcai.2025/534)

## Abstract

Recently, numerous methods have been proposed to enhance the robustness of the Graph Convolutional Networks (GCNs) for their vulnerability against adversarial attacks. Despite their empirical success, a significant gap remains in understanding GCNs' adversarial robustness from the theoretical perspective. This paper addresses this gap by analyzing generalization against both node and structure attacks for multi-layer GCNs through the framework of uniform stability.  Under the smoothness assumption of the loss function, we establish the first adversarial generalization bound of GCNs in expectation. Our theoretical analysis contributes to a deeper understanding of how adversarial perturbations and graph architectures influence generalization performance, which provides meaningful insights for designing robust models. Experimental results on benchmark datasets confirm the validity of our theoretical findings, highlighting their practical significance.