🤖 AI Summary
This work addresses critical challenges in graph neural networks (GNNs) concerning representation learning, generalization capability, and adversarial robustness. The authors propose a novel GNN framework that enhances representation learning through an improved graph shift operator, boosts generalization via an effective graph data augmentation strategy, and strengthens adversarial robustness by integrating orthogonalization with a controlled noise injection mechanism. Extensive experiments demonstrate that the proposed method consistently outperforms existing approaches across diverse tasks and scenarios. The framework not only achieves superior overall performance but also offers new theoretical insights and practical pathways for deploying robust and generalizable GNN models.
📝 Abstract
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.