This project conducted a comprehensive network analysis of a government website network, revealing key insights into its structure and dynamics. The analysis identified the website “22125” as the most influential node across multiple measures, underscoring its central importance in the network. The study also demonstrated that the network’s clustering coefficient is shaped by shared characteristics or interests among nodes rather than random chance. Additionally, the network exhibits high modularity, indicating a strong division into distinct communities. Using the Louvain method, 23 communities were identified, with 10 comprising a large number of nodes and the rest smaller subgroups. These findings highlight the presence of unique clusters within the network, each characterized by specific features or connections.