Graph Neural Networks: a bibliometrics overview

Abstract Recently, graph neural networks (GNN) have become a hot topic in machine learning community. This paper presents a Scopus-based bibliometric overview of the GNNs’ research since 2004, when GNN papers were first published. The study aims to evaluate GNN research trend, both quantitatively and qualitatively. We provide the trend of research, distribution of subjects, active and influential authors and institutions, sources of publications, most cited documents, and hot topics. Our investigations reveal that the most frequent subject categories in this field are computer science, engineering, telecommunications, linguistics, operations research and management science, information science and library science, business and economics, automation and control systems, robotics, and social sciences. In addition, the most active source of GNN publications is Lecture Notes in Computer Science. The most prolific or impactful institutions are found in the United States, China, and Canada. We also provide must-read papers and future directions. Finally, the application of graph convolutional networks and attention mechanism are now among hot topics of GNN research.