Applied System Innovations Using Recent Graph-Based Artificial Intelligence Techniques

Dear Colleagues,

In recent years, graph-based artificial intelligence techniques, e.g., graph embedding and graph neural networks (GNNs), have become the frontier of artificial intelligence and data science research, showing state-of-the-art performance in various applications. Graph-based methods have the advantage of representing non-Euclidean graph structures, which are universally seen in a wide range of systems and applications, e.g., industrial and control systems, finance, transportation, communication networks, electronic commerce applications, etc. For example, a road network is naturally a graph, with road intersections as the nodes and road connections as the edges. For another example, the buyers and sellers in an electronic commerce platform can be modelled with a bipartite graph. With graph structures as the input, many innovations have been achieved in those application scenarios with a superior performance compared to previous approaches. We believe this trend will continue in the next few years.

This Special Issue focuses on the applied system innovations, as well as the successful applications of graph-based artificial intelligence techniques in a wide range of engineering fields. This Special Issue is devoted to discussing recent developments in the broad field of graph-based innovations and their applications.

Topics of interest include but are not limited to:

• Graph-based innovations for industrial and control systems, e.g., model predictive control;
• Graph-based innovations for financial applications, e.g., stock market prediction;
• Graph-based innovations for communication networks, e.g., intrusion detection, mobile traffic prediction;
• Graph-based innovations for transportation applications, e.g., road traffic prediction, traffic data imputation;
• Graph-based innovations for electronic commerce applications, e.g., recommendation systems, fraud detection;
• Other relevant innovations with graph-based artificial intelligence methods.

Dr. Weiwei Jiang
Guest Editor

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• artificial intelligence
• machine learning
• deep learning
• graph embedding
• graph convolutional network
• graph attention network