Advances in Graph Neural Networks

Introduction.- Fundamental Graph Neural Networks.- Homogeneous Graph Neural Networks.- Heterogeneous Graph Neural Networks.- Dynamic Graph Neural Networks.- Hyperbolic Graph Neural Networks.- Distilling Graph Neural Networks.-  Platforms and Practice of Graph Neural Networks.- Future Direction and Conclusion.- References. 

 

Chuan Shi, PhD., is a Professor and Deputy Director of Beijing Key Lab of Intelligent Telecommunications Software and Multimedia at the Beijing University of Posts and Telecommunications. He received his B.S. from Jilin University in 2001, his M.S. from Wuhan University in 2004, and his Ph.D. from the ICT of Chinese Academic of Sciences in 2007. His research interests include data mining, machine learning, and evolutionary computing. He has published more than 100 papers in refereed journals and conferences.
Xiao Wang, Ph.D., is an Associate Professor in the School of Computer Science at the Beijing University of Posts and Telecommunications. He received his Ph.D. from the School of Computer Science and Technology at Tianjin University in 2016. He was a postdoctoral researcher in the Department of Computer Science and Technology at Tsinghua University. His current research interests include data mining, social network analysis, and machine learning. He has published more than 70 papers in refereed journals and conferences.
Cheng Yang, Ph.D., is an Associate Professor at the Beijing University of Posts and Telecommunications. He received his B.E. and Ph.D. from Tsinghua University in 2014 and 2019, respectively. His research interests include natural language processing and network representation learning. He has published more than 20 top-level papers in international journals and conferences including ACM TOIS, EMNLP, IJCAI, and AAAI.