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Optimal resilience of modular interacting networks

Urheber*innen

Dong,  Gaogao
External Organizations;

Wang,  Fan
External Organizations;

Shekhtman,  Louis M.
External Organizations;

Danziger,  Michael M.
External Organizations;

/persons/resource/Jingfang.Fan

Fan,  Jingfang
Potsdam Institute for Climate Impact Research;

Du,  Ruijin
External Organizations;

Liu,  Jianguo
External Organizations;

Tian,  Lixin
External Organizations;

Stanley,  H. Eugene
External Organizations;

Havlin,  Shlomo
External Organizations;

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Zitation

Dong, G., Wang, F., Shekhtman, L. M., Danziger, M. M., Fan, J., Du, R., Liu, J., Tian, L., Stanley, H. E., Havlin, S. (2021): Optimal resilience of modular interacting networks. - Proceedings of the National Academy of Sciences of the United States of America (PNAS), 118, 22, e1922831118.
https://doi.org/10.1073/pnas.1922831118


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_26629
Zusammenfassung
Coupling between networks is widely prevalent in real systems and has dramatic effects on their resilience and functional properties. However, current theoretical models tend to assume homogeneous coupling where all the various subcomponents interact with one another, whereas real-world systems tend to have various different coupling patterns. We develop two frameworks to explore the resilience of such modular networks, including specific deterministic coupling patterns and coupling patterns where specific subnetworks are connected randomly. We find both analytically and numerically that the location of the percolation phase transition varies nonmonotonically with the fraction of interconnected nodes when the total number of interconnecting links remains fixed. Furthermore, there exists an optimal fraction r∗ of interconnected nodes where the system becomes optimally resilient and is able to withstand more damage. Our results suggest that, although the exact location of the optimal r∗ varies based on the coupling patterns, for all coupling patterns, there exists such an optimal point. Our findings provide a deeper understanding of network resilience and show how networks can be optimized based on their specific coupling patterns.