Eigenvector Centrality

Eigenvector centrality is a graph-theoretic measure quantifying the influence of nodes within a network, identifying those most central to information flow or interaction. Current research focuses on improving its computation efficiency for large networks, particularly using graph neural networks and tensor methods, and applying it to diverse domains including social network analysis, federated learning, and the analysis of biological and criminal networks. These advancements enhance the accuracy and scalability of centrality analysis, leading to improved insights in various fields ranging from identifying influential individuals in criminal organizations to optimizing the performance of decentralized machine learning systems.