Graph Metrics for Temporal Networks
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Abstract (arXiv)
Temporal networks, i.e., networks in which the interactions among a set of elementary units change over time, can be modelled in terms of time-varying graphs, which are time-ordered sequences of graphs over a set of nodes. In such graphs, the concepts of node adjacency and reachability crucially depend on the exact temporal ordering of the links. Consequently, all the concepts and metrics proposed and used for the characterisation of static complex networks have to be redefined or appropriately extended to time-varying graphs, in order to take into account the effects of time ordering on causality. In this chapter we discuss how to represent temporal networks and we review the definitions of walks, paths, connectedness and connected components valid for graphs in which the links fluctuate over time. We then focus on temporal node-node distance, and we discuss how to characterise link persistence and the temporal small-world behaviour in this class of networks. Finally, we discuss the extension of classic centrality measures, including closeness, betweenness and spectral centrality, to the case of time-varying graphs, and we review the work on temporal motifs analysis and the definition of modularity for temporal graphs.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2013.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 2; 2025: 1.
6 papers.
- 2023A Network Science perspective of Graph Convolutional Networks: A surveycited by 1
- 2023DBGDGM: Dynamic Brain Graph Deep Generative Modelcited by 1
- 2024A Stochastic Geo-spatiotemporal Bipartite Network to Optimize GCOOS Sensor Placement Strategiescited by 1
- 2024STROOBnet Optimization via GPU-Accelerated Proximal Recurrence Strategiescited by 1
- 2025Quality Measures for Dynamic Graph Generative Modelscited by 1
- 2021Frequent Pattern Mining in Continuous-time Temporal Networkscited by 0
- 2025Quality Measures for Dynamic Graph Generative Models3 Mar 2025
- 2024STROOBnet Optimization via GPU-Accelerated Proximal Recurrence Strategies22 Apr 2024
- 2024A Stochastic Geo-spatiotemporal Bipartite Network to Optimize GCOOS Sensor Placement Strategies22 Apr 2024
- 2023DBGDGM: Dynamic Brain Graph Deep Generative Model26 Jan 2023
- 2023A Network Science perspective of Graph Convolutional Networks: A survey12 Jan 2023
- 2021Frequent Pattern Mining in Continuous-time Temporal Networks12 May 2021