A variety of phenomena and systems are usually regraded as networks with a set of nodes and relations. Diversified methods and models have been developed for different tasks on networks, such as structural discovery, link prediction, and anomaly detection. With the growing data scale due to the information explosion and scientific development, network structural modeling has become an interesting and new field of research. Furthermore, building and utilizing new technologies for efficient learning, inference, and predication on different types of networks has become the trend to look forward to.
In recent years, all kinds of machine-learning technologies on networks have been developed rapidly, especially network embedding and graph neural networks. They induce various methods and achieve satisfactory performance on network tasks, including node clustering or classification and link prediction. However, network structural modeling on real big data is usually lacking. Specialized modeling and learning methods for more complex (dynamic, text-rich, and heterogeneous) networks are also our concerns.
This special issue aims to provide a forum for the most recent advances in network structural modeling and learning with applications in real big data. We encourage original research works in network modeling and learning, including new deep-learning methods, probability graph models, and their applications. We also seek review articles in this field. Topics include, but are not limited to, the following:
Submission deadline: July 31, 2021
Notice of the first-round review results: September 31, 2021
Revision due: December 31, 2021
Final notice of accept/reject: April 31, 2022
Di Jin, Tianjin University, China. jindi@tju.edu.cn
Wenjun Wang, Tianjin University, China. wjwang@tju.edu.cn
Guojie Song, Peking University, China. gjsong@pku.edu.cn
Philip S. Yu, University of Illinois at Chicago, USA. psyu@uic.edu
Jiawei Han, University of Illinois at Urbana-Champaign, USA. hanj@illinois.edu
