The paper discusses the challenges encountered by Graph Neural Networks (GNNs) in real-world scenarios, including data imbalance, noise, privacy concerns, and out-of-distribution issues. It reviews existing models and presents solutions to improve the performance of GNNs in practical applications. The study provides insights into how GNNs can be optimized to address these challenges effectively.
Significant strides have been made in leveraging Graph Neural Networks (GNNs) to achieve success across various domains such as social network analysis, biochemistry, financial fraud detection, and network security. However, real-world training environments often lead to performance degradation due to factors like data imbalance, noise in erroneous data, privacy protection of sensitive information, and generalization capability for out-of-distribution scenarios.
To tackle these challenges, efforts have been devoted to improving the performance of GNN models in practical scenarios while enhancing their reliability and robustness. The paper systematically reviews existing GNN models focusing on solutions for the mentioned real-world challenges that many previous reviews have not considered.
Different strategies are proposed to address these challenges including re-balancing methods, augmentation-based techniques, and module improvement approaches. The study aims to provide a comprehensive overview of current landscape while outlining future research directions to enhance the reliability and robustness of GNN models in practical applications.
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by Wei Ju,Siyu ... at arxiv.org 03-08-2024
https://arxiv.org/pdf/2403.04468.pdfDeeper Inquiries