Research Details

Research information

Developed and simulated preferential attachment models to study network evolution, focusing on the "rich-get-richer" dynamic inherent in complex networks. Implemented simulations in Python using libraries like NetworkX for network generation and Matplotlib for visualization. Applied Bayesian inference techniques, including Markov Chain Monte Carlo (MCMC) methods and Approximate Bayesian Computation (ABC), to estimate model parameters and align simulations with observed network characteristics. Analyzed network formation processes by comparing simulated degree distributions with empirical data and utilized statistical methods to validate the accuracy of the models.