Applied System Innovations Using 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 vital to artificial intelligence and data science research, demonstrating state-of-the-art performances in various applications. Graph-based methods can represent non-Euclidean graph structures, which are universally seen in a wide range of systems and applications, such as industrial and control systems, finance, transportation, communication networks, electronic commerce applications, etc. For example, a road network is naturally a graph, with road intersections acting as the nodes and road connections as the edges. For example, the buyers and sellers on 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 superior performances compared to previous approaches. We believe that this trend will continue in the next few years.

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

Topics of interest include, but are not limited to, the following:

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

Dr. Weixi Gu
Dr. Weiwei Jiang
Guest Editors

Manuscript Submission Information

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