ArticleNature communications2019
Bond percolation in coloured and multiplex networks.
Article in Nature communications, 2019. 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.
- Homophily within and across groups.Nature communications · 2025Article
- The dynamic nature of percolation on networks with triadic interactions.Nature communications · 2023Article
- Computational prediction of the molecular configuration of three-dimensional network polymers.Nature materials · 2021Article
- The Radically Embodied Conscious Cybernetic Bayesian Brain: From Free Energy to Free Will and Back Again.Entropy (Basel, Switzerland) · 2021Article
- Generative Algorithm for Molecular Graphs Uncovers Products of Oil Oxidation.Journal of chemical information and modeling · 2021Article
- Robustness and lethality in multilayer biological molecular networks.Nature communications · 2020Article
- Modelling electrical conduction in nanostructure assemblies through complex networks.Nature materials · 2020Article
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Percolation in complex networks is a process that mimics network degradation and a tool that reveals peculiarities of the network structure. During the course of percolation, the emergent properties of networks undergo non-trivial transformations, which include a phase transition in the connectivity, and in some special cases, multiple phase transitions. Such global transformations are caused by only subtle changes in the degree distribution, which locally describe the network. Here we establish a generic analytic theory that describes how structure and sizes of all connected components in the network are affected by simple and colour-dependent bond percolations. This theory predicts locations of the phase transitions, existence of wide critical regimes that do not vanish in the thermodynamic limit, and a phenomenon of colour switching in small components. These results may be used to design percolation-like processes, optimise network response to percolation, and detect subtle signals preceding network collapse.
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Registered trials
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