Centrality Measure

Centrality measures quantify the importance of nodes within a network, aiming to identify influential or critical elements. Current research focuses on developing efficient algorithms, such as optimized LexRank variants, for large-scale networks and integrating centrality measures into machine learning models, particularly graph neural networks (GNNs), to improve prediction accuracy. These advancements are significant for diverse applications, including social network analysis, understanding information diffusion in decentralized systems, and enhancing the performance of graph-based machine learning algorithms. The development of novel centrality measures and their integration into broader analytical frameworks continues to be a key area of investigation.