Recruitment dynamics in adaptive social networks

Area of Science:

• Network Science
• Computational Social Science
• Mathematical Biology

Background:

• Social networks evolve dynamically with individuals joining (birth) and leaving (death).
• Recruitment processes, where susceptible individuals adopt a new status upon contact, are fundamental to network evolution.
• Adaptive behaviors, where individuals modify connections to enhance recruitment, introduce complexity to network dynamics.

Purpose of the Study:

• To model and analyze recruitment dynamics in adaptive social networks with birth and death processes.
• To investigate how network structure adaptation influences recruitment success and network topology.
• To identify different recruitment regimes based on node susceptibility and adaptation parameters.

Main Methods:

• Development of a mean-field theory to predict the growth threshold of the recruiting class.
• Analysis of the impact of an adaptation parameter on recruitment levels and network topology.
• Comparison of theoretical predictions with direct simulations of the adaptive network model.

Main Results:

• The growth threshold of the recruiting class is dependent on the network adaptation parameter.
• Network adaptation influences both the overall recruitment level and the resulting network topology.
• Two distinct parameter regimes were identified, leading to qualitatively different bifurcation diagrams based on node susceptibility frequency.

Conclusions:

• Adaptive network structures significantly alter recruitment dynamics compared to static networks.
• The frequency of node susceptibility plays a critical role in determining the macroscopic behavior of recruitment in adaptive networks.
• The findings provide a theoretical framework and simulation-based evidence for understanding complex recruitment phenomena in evolving social systems.