Centrality Measures
Centrality measures are a set of algorithms used to identify the most important nodes in a network. In data science, centrality measures are used to analyze the structure of complex networks, such as social networks, transportation networks, and biological networks. Centrality measures can be classified into three categories: degree centrality, betweenness 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. Eigenvector centrality measures the influence of a node based on the influence of its neighbors. Centrality measures are useful in identifying key players in a network, predicting the spread of information or disease, and understanding the overall structure of a network.
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