ArticleScientific reports2015
Quantifying dynamical spillover in co-evolving multiplex networks.
Article in Scientific reports, 2015. 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.
- Article
- Institutional similarity drives cultural similarity among online communities.Scientific reports · 2022Article
- Multilayer network analyses as a toolkit for measuring social structure.Current zoology · 2021Article
- Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net.Empirical software engineering · 2021Article
- The use of multilayer network analysis in animal behaviour.Animal behaviour · 2019Article
- Partial Shocks on Cooperative Multiplex Networks with Varying Degrees of Noise.Scientific reports · 2018Article
- Effects of temporal correlations in social multiplex networks.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiplex networks (a system of multiple networks that have different types of links but share a common set of nodes) arise naturally in a wide spectrum of fields. Theoretical studies show that in such multiplex networks, correlated edge dynamics between the layers can have a profound effect on dynamical processes. However, how to extract the correlations from real-world systems is an outstanding challenge. Here we introduce the Multiplex Markov chain to quantify correlations in edge dynamics found in longitudinal data of multiplex networks. By comparing the results obtained from the multiplex perspective to a null model which assumes layers in a network are independent, we can identify real correlations as distinct from simultaneous changes that occur due to random chance. We use this approach on two different data sets: the network of trade and alliances between nation states, and the email and co-commit networks between developers of open source software. We establish the existence of "dynamical spillover" showing the correlated formation (or deletion) of edges of different types as the system evolves. The details of the dynamics over time provide insight into potential causal pathways.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.