ArticleScientific reports2017
Burstiness and tie activation strategies in time-varying social networks.
Article in Scientific reports, 2017. 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.
- Unifying non-Markovian dynamics and agent heterogeneity in scalable stochastic networks.Nature communications · 2026Article
- Constructing temporal networks with bursty activity patterns.Nature communications · 2023Article
- Stochastic sampling effects favor manual over digital contact tracing.Nature communications · 2021Article
- Using Sensors in Organizational Research-Clarifying Rationales and Validation Challenges for Mixed Methods.Frontiers in psychology · 2019Article
- Epidemic spreading in modular time-varying networks.Scientific reports · 2018Article
- Epidemic spreading on activity-driven networks with attractiveness.Physical review. E · 2017Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent developments in the field of social networks shifted the focus from static to dynamical representations, calling for new methods for their analysis and modelling. Observations in real social systems identified two main mechanisms that play a primary role in networks' evolution and influence ongoing spreading processes: the strategies individuals adopt when selecting between new or old social ties, and the bursty nature of the social activity setting the pace of these choices. We introduce a time-varying network model accounting both for ties selection and burstiness and we analytically study its phase diagram. The interplay of the two effects is non trivial and, interestingly, the effects of burstiness might be suppressed in regimes where individuals exhibit a strong preference towards previously activated ties. The results are tested against numerical simulations and compared with two empirical datasets with very good agreement. Consequently, the framework provides a principled method to classify the temporal features of real networks, and thus yields new insights to elucidate the effects of social dynamics on spreading processes.
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