Guest Editorial: Special Issue on Network Structural Modeling and Learning in Big Data

The papers in this special section focus on the most recent advances in network structural modeling and learning with applications in the big data field. 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 the 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. Furthermore, network structural modeling on real big data is also interesting.