Mesoscale Clustering from Local Reconnection Dynamics in a Minimal Stochastic Network Model

This archive contains the full dataset, analysis scripts, and manuscript associated with the study: "Mesoscale Clustering from Local Reconnection Dynamics in a Minimal Stochastic Network Model." Contents:- manuscript (PDF and LaTeX source)- figures used in the paper- processed datasets from parameter sweeps and control experiments- run-level data including event logs and step metrics The study investigates whether mesoscale clustering can emerge in a minimal stochastic network governed by local rupture and reconnection dynamics. Key findings:- clustering near C ≈ 0.30 emerges in a narrow parameter regime (β ≈ 3.5, r ≈ 0.18)- removing the scalar field reduces clustering significantly (C ≈ 0.23, p = 0.010)- random-direction control shows no significant difference from guided dynamics All results are reproducible using the included data and code.