Trustworthy Graph Neural Networks: Models and Applications, 2nd Edition

Dear Colleagues,

In the era of big data, graph data have attracted considerable attention. We have witnessed the impressive performance of graph neural networks (GNNs) in dealing with graph data, as well as their use in various real-world applications (e.g., recommender systems, molecular property prediction). The increasing number of works on GNNs indicates a global trend in both the academic and industrial communities. Despite the progress made in GNNs, there are various open, unexplored, and unidentified challenges. One major concern is whether current GNNs are trustworthy. This is an inescapable problem when GNNs are used in real-world applications, especially in risk-sensitive domains. To address this problem, we need to make GNNs more robust, explainable, and stable. Thus, there is a pressing demand for novel and advanced trustworthy GNNs. In this Special Issue, our goal is to bring together researchers and practitioners working in the areas of GNNs to address a wide range of theoretical and practical issues.

Dr. Zhao Kang
Prof. Dr. Xiao Wang
Guest Editors

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• graph neural networks
• deep learning for graphs
• graph representation learning
• spectral graph theory
• robust graph neural networks
• explainable graph neural networks
• stable graph neural networks
• uncertainty in graph neural networks
• graph-neural-network-related applications

• Trustworthy Graph Neural Networks: Models and Applications in Mathematics (9 articles)