Complex Network Project

This project explores the structural properties of a brain network using network tools for analysis. We look at different network aspects, like degree distribution, centrality quantities, small-world features, and community detection algorithms. We found, through our analysis, that the brain network exhibits characteristics of a small-world network. This makes good information processing possible thanks to high clustering and short average path lengths. Our community detection algorithms found clear groups within the network. The Louvain technique and greedy optimization algorithms gave us the most consistent results. Visualizing the network shows the interconnected nature of communities, with a clear center area where nodes exhibit high values of different centrality measures. Overall, our study gives a deeper understanding of how the brain network is ordered. We emphasize its complex but efficient design.