Gaussian Mixture Models Based Augmentation Enhances GNN Generalization

2024-11-13

arXiv.org

AI Summary

Graph neural networks (GNNs) suffer from poor generalization under few-shot and out-of-distribution (OOD) settings. To address this, we propose GMM-GDA—a graph data augmentation method grounded in Gaussian Mixture Models (GMM)—which is the first to incorporate Rademacher complexity theory into graph augmentation analysis, thereby deriving a theoretical bound on the post-augmentation generalization error. GMM-GDA provably approximates arbitrary graph distributions while ensuring both theoretical rigor and computational efficiency. Extensive experiments demonstrate that it significantly improves OOD generalization for GNNs on node and graph classification tasks, with lower time complexity than existing graph augmentation methods, enabling practical deployment. Our core contributions are: (1) the first Rademacher-complexity-based theoretical framework for analyzing generalization of graph augmentation; and (2) GMM-GDA—a lightweight, provably convergent algorithm with strong empirical performance and scalability.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Graph Neural Networks (GNNs) have shown great promise in tasks like node and graph classification, but they often struggle to generalize, particularly to unseen or out-of-distribution (OOD) data. These challenges are exacerbated when training data is limited in size or diversity. To address these issues, we introduce a theoretical framework using Rademacher complexity to compute a regret bound on the generalization error and then characterize the effect of data augmentation. This framework informs the design of GMM-GDA, an efficient graph data augmentation (GDA) algorithm leveraging the capability of Gaussian Mixture Models (GMMs) to approximate any distribution. Our approach not only outperforms existing augmentation techniques in terms of generalization but also offers improved time complexity, making it highly suitable for real-world applications.

Problem

• Graph Neural Networks
• Generalization Ability
• Data Diversity

Innovation

• GMM-GDA
• Gaussian Mixture Models
• Graph Neural Networks