2024 IEEE TNNLS Distinguished Paper Award

Congratulations to Prof Shirui Pan and the team for winning the 2024 IEEE TNNLS Distinguished Paper Award. The full citation of the paper follows.

Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P.S. (2021). A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4-24.

This paper, published in IEEE Trans. Neural Networks Learn. Syst., in 2021, is a high-quality paper. It has become the most popular article in the journal since its publication. The article provides a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. The authors propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks. They further discuss the applications of graph neural networks across various domains and summarize the open source codes, benchmark data sets, and model evaluation of graph neural networks. Finally, the authors propose potential research directions in this rapidly growing field.

The research article has made a significant contribution to the field of graph neural networks. It investigates the challenges of learning from complex graph data, which has become increasingly prevalent in various applications. The proposed taxonomy provides a clear understanding of the state-of-the-art GNNs, which is helpful for researchers and practitioners to develop new models and applications. The article has been widely cited in the research community, with 8,000+ Google Scholar citations.