Process / pipeline
Temporal Network Analysis — Dynamic Networks
Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system.
Open in MethodMindSoonVideoSoon
Read the full method
Members only
Sign inSign in with a free account to read this section.
Sources
- Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125. DOI: 10.1016/j.physrep.2012.03.001 ↗
- Masuda, N. & Lambiotte, R. (2016). A Guide to Temporal Networks. World Scientific. DOI: 10.1142/9781786341426 ↗
Related methods
Referenced by
Bayesian Temporal Network AnalysisDynamic Community DetectionDynamic Degree CentralityDynamic Ego Network AnalysisDynamic Eigenvector CentralityDynamic Exponential Random Graph ModelDynamic Modularity AnalysisDynamic PageRankDynamic Stochastic Block ModelDynamic Two-Mode Network AnalysisEgo Network AnalysisGraph Neural Network (Network Analysis)Multilayer Network AnalysisMultilayer Social Network AnalysisMultilayer Temporal Network AnalysisNetwork Diffusion ModelsNetwork Resilience AnalysisSmall-World and Scale-Free Network AnalysisTemporal Community DetectionTemporal Multiplex Network AnalysisTemporal Two-Mode Network AnalysisWeighted Temporal Network Analysis