Betweenness Centrality

Betweenness centrality measures a node's importance in a network by quantifying its role as a bridge in shortest paths between other nodes. Current research focuses on improving the efficiency of betweenness centrality calculations for large networks, employing techniques like graph neural networks and sampling-based approximation algorithms to overcome computational limitations. This metric finds broad application in diverse fields, including community detection, network dismantling, and understanding information diffusion, with recent work highlighting its use in analyzing social media dynamics, optimizing transportation networks, and even improving machine learning model training in decentralized settings.