# Table 3 Definition of network measures

Network measures Definitions
Degree centrality The most intuitive notion of centrality focuses on the degree. The more edges an actor has, the more important it is
Betweenness centrality Counts the number of shortest paths between two nodes on which a given actor resides
Closeness centrality An actor is considered important if it is relatively close to all other actors. Closeness is based on the inverse of the distance of each actor to every other actor in the network
Eigenvector centrality Indicates that a given node has a relationship with other valuable nodes. A high eigenvector value for an actor means that a node has several neighbors with high eigenvector values
Eccentricity The eccentricity of node v is calculated by computing the shortest path between node v and all other nodes in the graph; then the longest shortest path is chosen
Authority Exhibits a node pointed to by many good hubs
Hub Exhibits a node that points to many good authorities
PageRank Assigns a numerical weight to each element of a hyperlinked set of documents, such as the World Wide Web, with the purpose of “measuring” its relative importance within the set
Clustering coefficient Quantifies how close neighbors are to being a clique. A clique is a subset of all of the edges connecting pairs of vertices of an undirected graph
1. Network measures include degree, betweenness, closeness centrality, and efficiency 