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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Funnel theorems for spreading on networks.

Gadi Fibich1, Tomer Levin1, Steven Schochet1

  • 1Department of Applied Mathematics, Tel Aviv University, Tel Aviv, Israel.

Chaos (Woodbury, N.Y.)
|July 7, 2025
PubMed
Summary
This summary is machine-generated.

New funnel theorems bound susceptibility probabilities in Bass and susceptible-infected models on networks. These theorems offer precise calculations for adoption and infection levels in various network structures.

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Area of Science:

  • Network Science
  • Mathematical Modeling
  • Epidemiology

Background:

  • Understanding information and disease propagation on networks is crucial.
  • Existing models often lack precise analytical solutions for complex network structures.

Purpose of the Study:

  • To derive and present novel funnel theorems for Bass and susceptible-infected models.
  • To provide bounds for susceptibility probabilities on modified networks.
  • To enable exact expressions for adoption/infection probabilities and levels.

Main Methods:

  • Derivation of funnel theorems for network models.
  • Analysis of susceptibility probabilities on modified networks with restricted incoming edges.
  • Application to networks with and without cycles.

Main Results:

  • Funnel theorems establish lower and upper bounds for susceptibility differences.
  • The theorems allow for simplification by considering single-edge influence.
  • Exact explicit expressions for adoption/infection probabilities are obtained.

Conclusions:

  • The derived funnel theorems offer a powerful analytical tool for network models.
  • These theorems facilitate precise calculations of adoption and infection dynamics.
  • The approach is applicable to diverse network types, enhancing predictive capabilities.