Centrality Analysis

Centrality Analysis is a set of methods used to identify the most important nodes or actors in a network. In Data Science, Centrality Analysis is used to measure the relative importance of nodes in a network based on their connectivity and influence. There are different types of centrality measures, including degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Degree centrality measures the number of connections a node has, while betweenness centrality measures the extent to which a node lies on the shortest path between other nodes. Closeness centrality measures how close a node is to all other nodes in the network, and eigenvector centrality measures the influence of a node based on the influence of its neighbors. Centrality Analysis is used in various applications, such as social network analysis, recommendation systems, and identifying key players in a network.