Recruitment dynamics in adaptive social networks

TL;DR

This paper models recruitment in adaptive social networks considering birth and death processes, analyzing how network adaptation influences recruitment thresholds, levels, and topology through mean field theory and simulations.

Contribution

It introduces a mean field model for recruitment dynamics in adaptive networks with birth and death, revealing how adaptation affects recruitment thresholds and network structure.

Findings

Identification of two distinct bifurcation regimes based on node susceptibility frequency.

Theoretical predictions align with simulation results.

Network adaptation significantly influences recruitment success and topology.

Abstract

We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime).