ArticleScientific reports2017
Effects of temporal correlations in social multiplex 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 7 papers.
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Who cites it
7 citing papers in PubMed.
- STR-GNN: stability-regularized graph neural networks for suppressing spurious temporal variations in dynamic community detection.Scientific reports · 2026Article
- Bell correlations outside physics.Scientific reports · 2023Article
- Neural Synchrony and Network Dynamics in Social Interaction: A Hyper-Brain Cell Assembly Hypothesis.Frontiers in human neuroscience · 2022Article
- Modeling partial lockdowns in multiplex networks using partition strategies.Applied network science · 2021Article
- Article
- Coevolution spreading in complex networks.Physics reports · 2019Review
- Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries.Frontiers in big data · 2019Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multi-layered networks represent a major advance in the description of natural complex systems, and their study has shed light on new physical phenomena. Despite its importance, however, the role of the temporal dimension in their structure and function has not been investigated in much detail so far. Here we study the temporal correlations between layers exhibited by real social multiplex networks. At a basic level, the presence of such correlations implies a certain degree of predictability in the contact pattern, as we quantify by an extension of the entropy and mutual information analyses proposed for the single-layer case. At a different level, we demonstrate that temporal correlations are a signature of a 'multitasking' behavior of network agents, characterized by a higher level of switching between different social activities than expected in a uncorrelated pattern. Moreover, temporal correlations significantly affect the dynamics of coupled epidemic processes unfolding on the network. Our work opens the way for the systematic study of temporal multiplex networks and we anticipate it will be of interest to researchers in a broad array of fields.
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Registered trials
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