This project intricately explores network analysis in the context of Alzheimer’s disease, emphasizing community detection and centrality as a pivotal facet in data research on complex networks. Community structure, an essential component of complex networks, has progressively emerged as a focus of study interest. The community detection algorithm is useful for examining the network’s true structure. The paper highlights the importance of community identification strategies in understanding network behavior and topology analysis by classifying them into non-overlapping and overlapping methods. Many ways are looked at, such as those built on different factors separation into parts, group linking, tag movement and gradual steps partitioning. The process of finding out what sicknesses, like Alzheimer’s disease someone has also highlights the advantages of knowing and working with many different abilities, skills working as a group to fix issues, and sharing what we know in a community. It present a way to look at community splits using a measure called modularity. This focuses on how it can be used to check layout, steer refinement, and comprehend network dynamics. In this paper, we look into measures of importance, especially coreness centrality. We also talk about core-periphery structures, k-shell decomposition, and extensions like the all-around score and hybrid centrality measures, which provide useful tools for determining influential nodes in complex networks.