keskeisyysanalyysillä

Keskeisyysanalyysi, known in English as centrality analysis, is a technique used in network science to identify the most important nodes within a network. Importance can be defined in various ways, leading to different types of centrality measures. Degree centrality, for example, quantifies importance by the number of connections a node has. Nodes with high degree centrality are directly connected to many other nodes. Betweenness centrality measures the extent to which a node lies on the shortest paths between other nodes. Nodes with high betweenness centrality act as bridges, controlling the flow of information or resources through the network. Closeness centrality assesses how close a node is to all other nodes in the network, typically by measuring the average shortest path distance. Nodes with high closeness centrality can quickly reach all other nodes. Eigenvector centrality, and its variation PageRank, consider the influence of a node's neighbors. A node is considered important if it is connected to other important nodes. These measures are applied across diverse fields, including social network analysis to identify influential individuals, biology to understand protein interaction networks, and transportation systems to pinpoint critical hubs. By understanding the structural role of nodes, keskeisyysanalyysi provides valuable insights into network dynamics and facilitates targeted interventions.