Tractable analytical models of adaptive networks with node turnover

Develop tractable analytical models for adaptive coevolving social networks that incorporate node turnover (entry and exit of users) rather than only link-weight dynamics, moving beyond agent-based simulations to enable analytical understanding of polarization and diffusion.

Background

Adaptive-network models are essential when the timescales of contagion and network evolution are comparable, as feedback between spreading and topology can drive polarization and fragmentation. Most existing analytical frameworks focus on evolving link weights and topologies but neglect node turnover, despite its centrality in real social systems.

Including node birth and death within analytically tractable models would allow rigorous paper of how churn coevolves with opinions and information diffusion, complementing simulation-based insights and enabling predictions of critical transitions.