The DOI matches its Crossref record; 173 of the 173 references it cites were found in a registry or an index.
Recent advances in percolation theory and its applications
Open accessAbstract
Percolation is the simplest fundamental model in statistical mechanics that exhibits phase transitions signaled by the emergence of a giant connected component. Despite its very simple rules, percolation theory has successfully been applied to describe a large variety of natural, technological and social systems. Percolation models serve as important universality classes in critical phenomena characterized by a set of critical exponents which correspond to a rich fractal and scaling structure of their geometric features. In this review we will first outline the basic features of the ordinary model and take a glimpse at a number of selective variations and modifications of the original model. Directed percolation process will be also discussed as a prototype of systems displaying a nonequilibrium phase transition. After a short review on SLE, we will provide an overview on existence of the scaling limit and conformal invariance of the critical percolation. We will also establish a connection with the magnetic models. Recent applications of the percolation theory in natural and artificial landscapes are also reviewed.
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Explosive transitions in complex networks’ structure and dynamics: Percolation and synchronization
Boccaletti, S., Almendral, J. A., Guan, S., Leyva, I., Liu, Z., Sendiña-Nadal, I., Wang, Z., & Zou, Y. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.10.004 · Cited by 363
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Metadata
- Authors
- Saberi, A. A.
- Venue
- Physics Reports
- Publisher
- Elsevier BV
- Published
- 2015-05
- Volume
- 578
- Pages
- 1-32
- ISSN
- 0370-1573 (ISSN-L)
- OpenAlex
- W2041740704
- Cited by
- 438
- References
- 173
- FWCI
- 58.71
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
