Funnel theorems for spreading on networks

We derive the funnel theorems for the Bass and susceptible-infected models on networks that describe the spreading of innovations and epidemics. Let j be a node and divide the remaining nodes into L≥2 disjoint sets {Al}l=1L. The funnel theorems provide lower and upper bounds for the difference between the susceptibility probability of j and the product of its susceptibility probability on the L modified networks in which j can only be influenced by incoming edges from Al. In particular, one can let L be equal to the indegree of j, so that in the modified networks, j is only influenced by one incoming edge. We illustrate how the funnel theorems can be used to obtain exact explicit expressions for the adoption/infection probabilities of nodes and for the expected adoption/infection level in various types of networks, both with and without cycles.

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Publication Details

Journal
Chaos An Interdisciplinary Journal of Nonlinear Science
Published
2025-07-01
DOI
https://doi.org/10.1063/5.0269855
Primary Topic
Complex Network Analysis Techniques
Type
article
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article

Funnel theorems for spreading on networks

Tomer Levin, Steven Schochet, Gadi Fibich
Chaos An Interdisciplinary Journal of Nonlinear Science
Complex Network Analysis Techniques
article

Funnel theorems for spreading on networks

Tomer Levin, Steven Schochet, Gadi Fibich
article en

Abstract

We derive the funnel theorems for the Bass and susceptible-infected models on networks that describe the spreading of innovations and epidemics. Let j be a node and divide the remaining nodes into L≥2 disjoint sets {Al}l=1L. The funnel theorems provide lower and upper bounds for the difference between the susceptibility probability of j and the product of its susceptibility probability on the L modified networks in which j can only be influenced by incoming edges from Al. In particular, one can let L be equal to the indegree of j, so that in the modified networks, j is only influenced by one incoming edge. We illustrate how the funnel theorems can be used to obtain exact explicit expressions for the adoption/infection probabilities of nodes and for the expected adoption/infection level in various types of networks, both with and without cycles.

Chaos An Interdisciplinary Journal of Nonlinear ScienceVol. 35(7)
Tel Aviv University (IL), Applied Mathematics (United States) (US)
Good health and well-being
Openalex Percentile: Top 87%
Complex Network Analysis Techniques
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