The DOI matches its Crossref record; 120 of the 120 references it cites were found in a registry or an index.
Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey
Open accessAbstract
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. To address the challenges resulting from the fact that this research crosses diverse fields as well as to survey dynamic graph neural networks, this work is split into two main parts. First, to address the ambiguity of the dynamic network terminology we establish a foundation of dynamic networks with consistent, detailed terminology and notation. Second, we present a comprehensive survey of dynamic graph neural network models using the proposed terminology.
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Cites
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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Emergence of Scaling in Random Networks
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GAN（Generative Adversarial Nets）
(2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,270
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A Comprehensive Survey on Graph Neural Networks
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Inductive Representation Learning on Large Graphs
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Cited by
Holzinger, A., Malle, B., Saranti, A., & Pfeifer, B. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.01.008 · Cited by 352
Structural-temporal coupling anomaly detection with dynamic graph transformer
Zong, C., Zhuang, Y., Shao, J., & Lu, W. (2026) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-025-01176-6 · Cited by 3
Yang, S., & Liu, X. (2026) · Journal of King Saud University Computer and Information Sciences · doi:10.1007/s44443-026-00904-2
Metadata
- Authors
- Skarding, J., Gabrys, B., & Musial, K.
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 79143-79168
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3024560045
- Cited by
- 331
- References
- 120
- FWCI
- 17.73
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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