Predicting network topology from empirical evolution rules: A case study in email networks

Understanding how macroscopic structures in complex systems emerge from microscopic rules remains a central challenge. Traditional approaches often rely on heuristic models rather than empirically-grounded mechanisms. This study establishes a closed-loop paradigm connecting data-driven mechanism discovery, mathematical modeling, and theoretical prediction. Through analysis of a temporal email network, we identify two precise microscopic rules: new connections form via linear preferential attachment with a constant baseline, while existing connections dissolve uniformly. We encode these empirical rules into a stochastic model and analytically derive the steady-state degree distribution. The theoretical prediction exhibits quantitative agreement with empirical observations, validating that the complex network topology directly emerges from the simple, measured dynamics. This work demonstrates a generalizable methodology for bridging microscopic behaviors and macroscopic organization in evolving systems.