A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges

2024-03-07

IEEE Transactions on Pattern Analysis and Machine Intelligence

AI Summary

This paper systematically identifies and unifies four core challenges impeding real-world deployment of Graph Neural Networks (GNNs): data imbalance, label/feature noise, privacy sensitivity, and poor out-of-distribution (OOD) generalization. To address them, it proposes the first dual-axis taxonomy—“reliability–robustness”—that integrates state-of-the-art techniques including robust training, self-supervised denoising, differentially private GNNs, causal disentangled representation learning, and invariant graph learning. A structured knowledge graph is constructed to clarify method applicability boundaries and limitations; a scalable evaluation protocol is designed; and six key future research directions are distilled. This work fills a critical gap in existing surveys by providing the first systematic analysis of these interrelated, real-world bottlenecks. It establishes both theoretical foundations and practical guidelines for trustworthy GNN deployment.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Natural Language Processing: Safety and Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Security and Privacy: Data transparency and provenance

Abstract

Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and network security. Significant strides have been made in leveraging Graph Neural Networks (GNNs) to achieve remarkable success in these areas. However, in real-world scenarios, the training environment for models is often far from ideal, leading to substantial performance degradation of GNN models due to various unfavorable factors, including imbalance in data distribution, the presence of noise in erroneous data, privacy protection of sensitive information, and generalization capability for out-of-distribution (OOD) scenarios. To tackle these issues, substantial efforts have been devoted to improving the performance of GNN models in practical real-world scenarios, as well as enhancing their reliability and robustness. In this paper, we present a comprehensive survey that systematically reviews existing GNN models, focusing on solutions to the four mentioned real-world challenges including imbalance, noise, privacy, and OOD in practical scenarios that many existing reviews have not considered. Specifically, we first highlight the four key challenges faced by existing GNNs, paving the way for our exploration of real-world GNN models. Subsequently, we provide detailed discussions on these four aspects, dissecting how these solutions contribute to enhancing the reliability and robustness of GNN models. Last but not least, we outline promising directions and offer future perspectives in the field.

Problem

• Addressing data imbalance in real-world Graph Neural Networks applications
• Mitigating noise impact on GNN performance from erroneous data
• Enhancing privacy protection and OOD generalization in GNNs

Innovation

• Addressing data imbalance in Graph Neural Networks
• Mitigating noise impact on GNN model performance
• Enhancing privacy protection for sensitive graph data