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Does Isolating High-Modularity Communities Prevent Cascading Failure?

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Complex Networks & Their Applications XII (COMPLEX NETWORKS 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1142))

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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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Notes

  1. 1.

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References

  1. Berahmand, K., Bouyer, A., Vasighi, M.: Community detection in complex networks by detecting and expanding core nodes through extended local similarity of nodes. IEEE Trans. Comput. Soc. Syst. 5(4), 1021–1033 (2018). https://doi.org/10.1109/TCSS.2018.2879494

    Article  Google Scholar 

  2. Domb, C.: Order-disorder statistics. ii. a two-dimensional model. Proc. Roy. Soc. Lond. Ser. A. Math. Phys. Sci. 199(1057), 199–221 (1949)

    Google Scholar 

  3. Dugué, N., Perez, A.: Directed Louvain: maximizing modularity in directed networks. Ph.D. thesis, Université d’Orléans (2015)

    Google Scholar 

  4. Eubank, S., Nath, M., Ren, Y., Adiga, A.: Perturbative methods for mostly monotonic probabilistic satisfiability problems. arXiv preprint arXiv:2206.03550 (2022)

  5. FAF: Freight Analysis Framework (FAF) version 5 (2022). https://faf.ornl.gov/faf5/

  6. FAO: Production and trade (2021). http://www.fao.org/faostat/en/#data

  7. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  8. Ghosh, R., Teng, S.H., Lerman, K., Yan, X.: The interplay between dynamics and networks: centrality, communities, and cheeger inequality. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1406–1415 (2014)

    Google Scholar 

  9. Gilligan, C.A.: Sustainable agriculture and plant diseases: an epidemiological perspective. Philos. Trans. Roy. Soc. B: Biol. Sci. 363(1492), 741–759 (2008)

    Article  Google Scholar 

  10. Gilligan, C.A., Gubbins, S., Simons, S.A.: Analysis and fitting of an SIR model with host response to infection load for a plant disease. Philos. Trans. Roy. Soc. Lond. Ser. B: Biol. Sci. 352(1351), 353–364 (1997)

    Google Scholar 

  11. Harenberg, S., et al.: Community detection in large-scale networks: a survey and empirical evaluation. Wiley Interdisc. Rev. Comput. Stat. 6(6), 426–439 (2014)

    Article  Google Scholar 

  12. Leicht, E.A., Newman, M.E.: Community structure in directed networks. Phys. Rev. Lett. 100(11), 118,703 (2008)

    Google Scholar 

  13. Malliaros, F.D., Vazirgiannis, M.: Clustering and community detection in directed networks: a survey. Phys. Rep. 533(4), 95–142 (2013)

    Article  MathSciNet  Google Scholar 

  14. Mishra, R., Eubank, S., Nath, M., Amundsen, M., Adiga, A.: Community detection using Moore-Shannon network reliability: application to food networks. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Micciche, S. (eds.) COMPLEX NETWORKS 2016 2022. SCI, vol. 1078, pp. 271–282. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-21131-7_21

    Chapter  Google Scholar 

  15. Moore, E., Shannon, C.: Reliable circuits using less reliable relays. J. Franklin Inst. 262(3), 191–208 (1956)

    Article  MathSciNet  Google Scholar 

  16. Nath, M., et al.: Using network reliability to understand international food trade dynamics. In: Aiello, L.M., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L.M. (eds.) COMPLEX NETWORKS 2018. SCI, vol. 812, pp. 524–535. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05411-3_43

    Chapter  Google Scholar 

  17. Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)

    Article  Google Scholar 

  18. Palmer, W.R., Zheng, T.: Spectral clustering for directed networks. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds.) COMPLEX NETWORKS 2020 2020. SCI, vol. 943, pp. 87–99. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-65347-7_8

    Chapter  Google Scholar 

  19. Roth, D.: On the hardness of approximate reasoning. Artif. Intell. 82(1), 273–302 (1996). https://www.sciencedirect.com/science/article/pii/0004370294000921

  20. Valiant, L.G.: The complexity of enumeration and reliability problems. SIAM J. Comput. 8(3), 410–421 (1979)

    Article  MathSciNet  Google Scholar 

  21. Wang, X., Liu, G., Li, J., Nees, J.P.: Locating structural centers: a density-based clustering method for community detection. PLoS ONE 12(1), 1–23 (2017). https://doi.org/10.1371/journal.pone.0169355

  22. Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977)

    Article  Google Scholar 

  23. Zhang, Y., Adhikari, B., Jan, S.T., Prakash, B.A.: Meike: influence-based communities in networks. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 318–326. SIAM (2017)

    Google Scholar 

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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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  • DOI: https://doi.org/10.1007/978-3-031-53499-7_4

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