Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey

Understand

Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems, and epidemiology.

• Representing complex networks as structures changing over time allow network models to leverage not only structural but also temporal patterns.
• However, as dynamic network literature stems from diverse fields and makes use of inconsistent terminology, it is challenging to navigate.
• Meanwhile, graph neural networks (GNNs) have gained a lot of attention in recent years for their ability to perform well on a range of network science tasks, such as link prediction and node classification.