提出三类方法提升图神经网络的表示能力、泛化性和抗攻击性。
Key Principles of Graph Machine Learning: Representation, Robustness, and Generalization
- 基于图移位算子设计新表示学习方法，增强跨场景性能。
- 通过图数据增强提升模型泛化能力，减少过拟合风险。
- 结合正交化与噪声防御，显著提高模型对抗攻击的鲁棒性。
图神经网络（GNNs）已成为从结构化数据中学习表示的强大工具。尽管其在各类应用中取得显著成功，但在泛化性能、对对抗扰动的鲁棒性以及表示学习有效性方面仍面临挑战。本文通过三项主要贡献系统研究这些问题：（1）基于图移位算子（GSOs）开发新型表示学习技术，以提升在多场景下的表现；（2）通过图数据增强提升泛化能力；（3）利用正交化与基于噪声的防御策略，构建更鲁棒的GNN。本工作为理解GNN的局限与潜力提供了更系统的理论基础。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations from structured data. Despite their growing popularity and success across various applications, GNNs encounter several challenges that limit their performance. in their generalization, robustness to adversarial perturbations, and the effectiveness of their representation learning capabilities. In this dissertation, I investigate these core aspects through three main contributions: (1) developing new representation learning techniques based on Graph Shift Operators (GSOs, aiming for enhanced performance across various contexts and applications, (2) introducing generalization-enhancing methods through graph data augmentation, and (3) developing more robust GNNs by leveraging orthonormalization techniques and noise-based defenses against adversarial attacks. By addressing these challenges, my work provides a more principled understanding of the limitations and potential of GNNs.
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