This paper examines the Huawei Social Network dataset using advanced network analysis techniques to identify influential users and uncover community structures. By constructing a social network graph and applying centrality measures such as degree, closeness, and eigenvector centrality, we were able to highlight key nodes that play a crucial role in the network’s communication and information flow. Furthermore, community detection algorithms like Louvain and Girvan–Newman uncovered distinct user clusters, offering valuable insights into group dynamics. These findings help in understanding user influence, behavior, and interactions within social networks and have potential applications in targeted marketing, influencer identification, and network optimization.