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Cited by 286

The DOI matches its Crossref record; 256 of the 256 references it cites were found in a registry or an index.

Coevolution spreading in complex networks

Open access
Wang, Wei, Liu, Quan-Hui, Liang, Junhao, Hu, Yanqing, Zhou, Tao·2019·Physics Reports
DOI 10.1016/j.physrep.2019.07.001
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Abstract

The propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic–awareness, and epidemic–resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed.

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Metadata

Authors
Wang, W., Liu, Q.-H., Liang, J., Hu, Y., & Zhou, T.
Venue
Physics Reports
Publisher
Elsevier BV
Published
2019-08
Volume
820
Pages
1-51
ISSN
0370-1573 (ISSN-L)
DOI
10.1016/j.physrep.2019.07.001
OpenAlex
W2909752832
Cited by
286
References
256
FWCI
16.53
Type
journal article
Language
en
Open access
green

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.