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

TL;DR

This survey addresses four real-world challenges of Graph Neural Networks: imbalance, noise, privacy, and out-of-distribution (OOD) behavior. It introduces a novel taxonomy and provides a thorough review of methods tailored to each challenge, including re-balancing, augmentation, representation learning, and robust optimization techniques. The work discusses limitations such as topology imbalance, privacy-utility trade-offs, and the need for scalable benchmarks and theoretical guarantees, offering concrete directions for future research. By connecting practical constraints to methodological advances, the paper underlines the potential for more reliable and interpretable GNNs in domains like finance, biology, and social networks.

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.

Figures (6)

• Figure 1: An illustrative example of GNN models handling practical social network scenarios. User data extracted from real-world platforms typically exhibit long-tailed distributions, indicating there are widespread mainstream user types alongside lots of rare genres. The interactions among users may be influenced by structural noises and the presence of fake labels. Moreover, the practical GNN models are confronted with attack models and user information leakage issues. The generalization of models from existing business scenarios to novel environments also introduces OOD concerns.
• Figure 2: An overview of the taxonomy for existing GNN models in real world.
• Figure 3: Illustration of the data imbalanced problem. The labels assigned to nodes or graphs that obtained from real-world data sources always suffer from severe class imbalance issue brought by the long-tail distribution of samples. The challenge calls for various applicable re-balancing strategies to train robust and reliable GNNs.
• Figure 4: Illustration of GNNs under the impact of label and structural noise. Inevitable label errors require the GNN model to accurately identify mislabeled samples, while the fake or absent edges between nodes require the model to reconstruct the ground-truth adjacency.
• Figure 5: Illustration of the attacks and defenses around both private data and model weights. The objective of the attack model is to extract private information from a target GNN. In response, the model needs to take measures and safeguard privacy from the attack model.