Abstract
Communities are often defined as sets of nodes that are more densely connected to each other than to those outside the community, i.e., high-modularity partitions. It seems obvious that isolating high-modularity communities is a good way to prevent the spread of cascading failures. Here we develop a heuristic approach informed by Moore-Shannon network reliability that focuses on dynamics rather than topology. It defines communities directly in terms of the size of cascades they allow. We demonstrate that isolating communities defined this way may control cascading failure better. Moreover, this approach is sensitive to the values of dynamical parameters and allows for problem-specific constraints such as cost.
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Or, for the culinarily challenged, from equilibrium statistical mechanics.
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Acknowledgments
The author would like to acknowledge M. Nath, R. Mishra, and A. Adiga for their many helpful discussions and for constructing the commodity networks and a framework for carrying out a nontrivial experimental design and analysis. This material is based upon work supported by the National Science Foundation under Grants No. CCF-1918656 and CNS-2041952 and by grant no. 2019-67021-29933, Network Models of Food Systems and their Application to Invasive Species Spread, from the USDA National Institute of Food and Agriculture.
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Eubank, S. (2024). Does Isolating High-Modularity Communities Prevent Cascading Failure?. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-031-53499-7_4
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