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Topological measures for identifying and predicting the spread of complex contagions

Author

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  • Douglas Guilbeault

    (Haas School of Business, The University of California, Berkeley)

  • Damon Centola

    (The Annenberg School for Communication, The University of Pennsylvania
    School of Engineering, The University of Pennsylvania)

Abstract

The standard measure of distance in social networks – average shortest path length – assumes a model of “simple” contagion, in which people only need exposure to influence from one peer to adopt the contagion. However, many social phenomena are “complex” contagions, for which people need exposure to multiple peers before they adopt. Here, we show that the classical measure of path length fails to define network connectedness and node centrality for complex contagions. Centrality measures and seeding strategies based on the classical definition of path length frequently misidentify the network features that are most effective for spreading complex contagions. To address these issues, we derive measures of complex path length and complex centrality, which significantly improve the capacity to identify the network structures and central individuals best suited for spreading complex contagions. We validate our theory using empirical data on the spread of a microfinance program in 43 rural Indian villages.

Suggested Citation

  • Douglas Guilbeault & Damon Centola, 2021. "Topological measures for identifying and predicting the spread of complex contagions," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24704-6
    DOI: 10.1038/s41467-021-24704-6
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    Cited by:

    1. Van Santen, Niels & Ryckebusch, Jan & Rocha, Luis E.C., 2024. "Social clustering reinforces external influence on the majority opinion model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 648(C).
    2. Keng, Ying Ying & Kwa, Kiam Heong, 2023. "Contagion in social networks: On contagion thresholds," Applied Mathematics and Computation, Elsevier, vol. 456(C).
    3. Daniel Reisinger & Fabian Tschofenig & Raven Adam & Marie Lisa Kogler & Manfred Füllsack & Fabian Veider & Georg Jäger, 2024. "Patterns of stability in complex contagions," Journal of Computational Social Science, Springer, vol. 7(2), pages 1895-1911, October.
    4. James Flamino & Alessandro Galeazzi & Stuart Feldman & Michael W. Macy & Brendan Cross & Zhenkun Zhou & Matteo Serafino & Alexandre Bovet & Hernán A. Makse & Boleslaw K. Szymanski, 2023. "Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections," Nature Human Behaviour, Nature, vol. 7(6), pages 904-916, June.
    5. Anwesha Sengupta & Shashankaditya Upadhyay & Indranil Mukherjee & Prasanta K. Panigrahi, 2024. "A study of the effect of influential spreaders on the different sectors of Indian market and a few foreign markets: a complex networks perspective," Journal of Computational Social Science, Springer, vol. 7(1), pages 45-85, April.
    6. Guilherme Ferraz de Arruda & Giovanni Petri & Pablo Martin Rodriguez & Yamir Moreno, 2023. "Multistability, intermittency, and hybrid transitions in social contagion models on hypergraphs," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    7. Roberto Araya, 2023. "Connecting Classrooms with Online Interclass Tournaments: A Strategy to Imitate, Recombine and Innovate Teaching Practices," Sustainability, MDPI, vol. 15(10), pages 1-25, May.
    8. Yann Bramoullé & Garance Genicot, 2024. "Diffusion and targeting centrality," Post-Print hal-04718273, HAL.
    9. Anwesha Sengupta & Shashankaditya Upadhyay & Indranil Mukherjee & Prasanta K. Panigrahi, 2022. "Describing the effect of influential spreaders on the different sectors of Indian market: a complex networks perspective," Papers 2303.05432, arXiv.org.

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