Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
- Publication Type:
- Journal Article
- Citation:
- 2020
- Issue Date:
- 2020-05-14
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| 2005.07496v1.pdf | Accepted Permission | 1.95 MB |
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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.
Despite the popularity of graph neural networks and the proven benefits of
dynamic network models, there has been little focus on graph neural networks
for dynamic networks. We aim to provide a review that demystifies dynamic
networks, introduces dynamic graph neural networks (DGNNs) and appeals to
researchers with a background in either network science or data science. We
contribute: (i) a comprehensive dynamic network taxonomy, (ii) a survey of
dynamic graph neural networks and (iii) an overview of how dynamic graph neural
networks can be used for dynamic link prediction.
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