Resilience of Networks with Community Structure Behaves as If under an External Field. Dong, G.; Fan, J.; Shekhtman, L. M.; Shai, S.; Du, R.; Tian, L.; Chen, X.; Stanley, H. E.; and Havlin, S. 115(27):6911–6915.
Resilience of Networks with Community Structure Behaves as If under an External Field [link]Paper  doi  abstract   bibtex   
[Significance] Much work has focused on phase transitions in complex networks in which the system transitions from a resilient to a failed state. Furthermore, many of these networks have a community structure, whose effects on resilience have not yet been fully understood. Here, we show that the community structure can significantly affect the resilience of the system in that it removes the phase transition present in a single module, and the network remains resilient at this transition. In particular, we show that the effect of increasing interconnections is analogous to increasing external magnetic field in spin systems. Our findings provide insight into the resilience of many modular complex systems and clarify the important effects that community structure has on network resilience. [Abstract] Although detecting and characterizing community structure is key in the study of networked systems, we still do not understand how community structure affects systemic resilience and stability. We use percolation theory to develop a framework for studying the resilience of networks with a community structure. We find both analytically and numerically that interlinks (the connections among communities) affect the percolation phase transition in a way similar to an external field in a ferromagnetic- paramagnetic spin system. We also study universality class by defining the analogous critical exponents δ and γ, and we find that their values in various models and in real-world coauthor networks follow the fundamental scaling relations found in physical phase transitions. The methodology and results presented here facilitate the study of network resilience and also provide a way to understand phase transitions under external fields.
@article{dongResilienceNetworksCommunity2018,
  title = {Resilience of Networks with Community Structure Behaves as If under an External Field},
  author = {Dong, Gaogao and Fan, Jingfang and Shekhtman, Louis M. and Shai, Saray and Du, Ruijin and Tian, Lixin and Chen, Xiaosong and Stanley, H. Eugene and Havlin, Shlomo},
  date = {2018-07},
  journaltitle = {Proceedings of the National Academy of Sciences},
  volume = {115},
  pages = {6911--6915},
  issn = {0027-8424},
  doi = {10.1073/pnas.1801588115},
  url = {https://doi.org/10.1073/pnas.1801588115},
  abstract = {[Significance] Much work has focused on phase transitions in complex networks in which the system transitions from a resilient to a failed state. Furthermore, many of these networks have a community structure, whose effects on resilience have not yet been fully understood. Here, we show that the community structure can significantly affect the resilience of the system in that it removes the phase transition present in a single module, and the network remains resilient at this transition. In particular, we show that the effect of increasing interconnections is analogous to increasing external magnetic field in spin systems. Our findings provide insight into the resilience of many modular complex systems and clarify the important effects that community structure has on network resilience.

[Abstract] Although detecting and characterizing community structure is key in the study of networked systems, we still do not understand how community structure affects systemic resilience and stability. We use percolation theory to develop a framework for studying the resilience of networks with a community structure. We find both analytically and numerically that interlinks (the connections among communities) affect the percolation phase transition in a way similar to an external field in a ferromagnetic- paramagnetic spin system. We also study universality class by defining the analogous critical exponents δ and γ, and we find that their values in various models and in real-world coauthor networks follow the fundamental scaling relations found in physical phase transitions. The methodology and results presented here facilitate the study of network resilience and also provide a way to understand phase transitions under external fields.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14613249,~to-add-doi-URL,analogical-reasoning,biodiversity,community-structure,emergent-property,feedback,mathematical-reasoning,networks,resilience,species-richness},
  number = {27}
}
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