An Introduction to Temporal Graphs: An Algorithmic Perspective
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Abstract (arXiv)
A temporal graph is, informally speaking, a graph that changes with time. When time is discrete and only the relationships between the participating entities may change and not the entities themselves, a temporal graph may be viewed as a sequence G1,G2ldots,Gl of static graphs over the same (static) set of nodes V. Though static graphs have been extensively studied, for their temporal generalization we are still far from having a concrete set of structural and algorithmic principles. Recent research shows that many graph properties and problems become radically different and usually substantially more difficult when an extra time dimension in added to them. Moreover, there is already a rich and rapidly growing set of modern systems and applications that can be naturally modeled and studied via temporal graphs. This, further motivates the need for the development of a temporal extension of graph theory. We survey here recent results on temporal graphs and temporal graph problems that have appeared in the Computer Science community.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Mar 2015.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 1; 2025: 2; 2026: 2.
10 papers.
- 2023The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecastingcited by 100
- 2022TIGGER: Scalable Generative Modelling for Temporal Interaction Graphscited by 18
- 2023Dynamic Graph Representation Learning with Neural Networks: A Surveycited by 15
- 2024Efficient Dynamic Attributed Graph Generationcited by 5
- 2026Temporal Graph Pattern Machinecited by 2
- 2023As Time Goes By: Adding a Temporal Dimension Towards Resolving Delegations in Liquid Democracycited by 1
- 2021GSA-Forecaster: Forecasting Graph-Based Time-Dependent Data with Graph Sequence Attentioncited by 0
- 2025Approximating Optimal Labelings for Temporal Connectivitycited by 0
- 2025Fair Minimum Labeling: Efficient Temporal Network Activations for Reachability and Equitycited by 0
- 2026Time-Varying Data as Sheaves: an Invitation to Narrativescited by 0
- 2026Time-Varying Data as Sheaves: an Invitation to Narratives8 Sep 2026
- 2026Temporal Graph Pattern Machine30 Jan 2026
- 2025Fair Minimum Labeling: Efficient Temporal Network Activations for Reachability and Equity4 Oct 2025
- 2025Approximating Optimal Labelings for Temporal Connectivity23 Apr 2025
- 2024Efficient Dynamic Attributed Graph Generation11 Dec 2024
- 2023As Time Goes By: Adding a Temporal Dimension Towards Resolving Delegations in Liquid Democracy24 Jul 2023
- 2023Dynamic Graph Representation Learning with Neural Networks: A Survey12 Apr 2023
- 2023The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting11 Apr 2023
- 2022TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs7 Mar 2022
- 2021GSA-Forecaster: Forecasting Graph-Based Time-Dependent Data with Graph Sequence Attention13 Apr 2021