Topological measures for identifying and predicting the spread of complex contagions

Douglas Guilbeault and Damon Centola ()
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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

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24704-6

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DOI: 10.1038/s41467-021-24704-6

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