2025/12/10 by Gaurav Koley, Koley, Gaurav, Sanika Digrajkar +1
Computer Science · Physics and Astronomy · #Recommender Systems and Techniques #Advanced Graph Neural Networks #Complex Network Analysis Techniques
paper · pdf · doi:10.48550/arxiv.2512.10106
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 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.