ArticleNature communications2023
Constructing temporal networks with bursty activity patterns.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Modelling plant disease spread via high-resolution human mobility networks.Journal of the Royal Society, Interface · 2026Article
- Gillespie-based simulation and inference for non-Markovian stochastic reaction networks.Briefings in bioinformatics · 2026Article
- Telecommunication-inspired network models of healthy and diseased brains.Scientific reports · 2026Article
- Unifying non-Markovian dynamics and agent heterogeneity in scalable stochastic networks.Nature communications · 2026Article
- Deterministic theory of evolutionary games on temporal networks.Journal of the Royal Society, Interface · 2024Article
- Constructing temporal networks with bursty activity patterns.Nature communications · 2023Article
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Authors and funding
5 authors.
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Abstract
Human social interactions tend to vary in intensity over time, whether they are in person or online. Variable rates of interaction in structured populations can be described by networks with the time-varying activity of links and nodes. One of the key statistics to summarize temporal patterns is the inter-event time, namely the duration between successive pairwise interactions. Empirical studies have found inter-event time distributions that are heavy-tailed, for both physical and digital interactions. But it is difficult to construct theoretical models of time-varying activity on a network that reproduce the burstiness seen in empirical data. Here we develop a spanning-tree method to construct temporal networks and activity patterns with bursty behavior. Our method ensures any desired target inter-event time distributions for individual nodes and links, provided the distributions fulfill a consistency condition, regardless of whether the underlying topology is static or time-varying. We show that this model can reproduce burstiness found in empirical datasets, and so it may serve as a basis for studying dynamic processes in real-world bursty interactions.
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