Studying how recommendation systems reshape social networks is difficult on live platforms: confounds abound, and controlled experiments risk user harm. We present an agent-based simulator where content production, tie formation, and a graph attention network (GAT) recommender co-evolve in a closed loop. We calibrate parameters using Mastodon data and validate out-of-sample against Bluesky (4--6\% error on structural metrics; 10--15\% on held-out temporal splits). Across 18 configurations at 100 agents, we find that \emph{activation timing} affects outcomes: introducing recommendations at t=10 vs.\ t=40 decreases transitivity by 10\% while engagement differs by <8\%. Delaying activation increases content diversity by 9\% while reducing modularity by 4\%. Scaling experiments (n up to 5,000) show the effect persists but attenuates. Jacobian analysis confirms local stability under bounded reactance parameters. We release configuration schemas and reproduction scripts.
@article{arxiv.2512.10106,
title = {A Simulation Framework for Studying Recommendation-Network Co-evolution in Social Platforms},
author = {Gaurav Koley and Sanika Digrajkar},
journal= {arXiv preprint arXiv:2512.10106},
year = {2025}
}