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Competition between simple and complex contagion on temporal networks

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Behavioral adoptions of individuals are influenced by their peers in different ways. While in some cases an individual may change behavior after a single incoming influence, in other cases multiple cumulated attempts of social influence are necessary for the same outcome. These two mechanisms, known as simple and complex contagion, often occur together in social contagion phenomena, yet their distinguishability based on the observable contagion dynamics is challenging. In this paper we define a social contagion model evolving on temporal networks where individuals can switch between contagion mechanisms. We explore three spreading scenarios: predominated by simple or complex contagion, or where the dominant mechanism changes during the unfolding process. We propose analytical and numerical methods relying on global spreading observables to identify which of these three scenarios characterizes a social spreading outbreak. This work offers insights into social contagion dynamics on temporal networks, without assuming prior knowledge about the contagion mechanism driving the adoptions of individuals.
Title: Competition between simple and complex contagion on temporal networks
Description:
Behavioral adoptions of individuals are influenced by their peers in different ways.
While in some cases an individual may change behavior after a single incoming influence, in other cases multiple cumulated attempts of social influence are necessary for the same outcome.
These two mechanisms, known as simple and complex contagion, often occur together in social contagion phenomena, yet their distinguishability based on the observable contagion dynamics is challenging.
In this paper we define a social contagion model evolving on temporal networks where individuals can switch between contagion mechanisms.
We explore three spreading scenarios: predominated by simple or complex contagion, or where the dominant mechanism changes during the unfolding process.
We propose analytical and numerical methods relying on global spreading observables to identify which of these three scenarios characterizes a social spreading outbreak.
This work offers insights into social contagion dynamics on temporal networks, without assuming prior knowledge about the contagion mechanism driving the adoptions of individuals.

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