Correlating centralities of social networks

Topic modelling and social network analysis of publications and patents in humanoid robot technology

“…This study uses SNA methods and analyses the co-occurrence and centrality measures to indicate influential keywords from DBs. We measured degree centrality (DC), closeness centrality (CC) and betweenness centrality (BC); these three indices are widely used in SNA [39,40]. DC of a node is the number of connections incident upon it and is an effective measure to calculate influential or important nodes within a network graph [40].…”

“…We measured degree centrality (DC), closeness centrality (CC) and betweenness centrality (BC); these three indices are widely used in SNA [39,40]. DC of a node is the number of connections incident upon it and is an effective measure to calculate influential or important nodes within a network graph [40]. We normalised DC by dividing the centrality value by the maximum possible degree ( n − 1) as…”

“…CC of a node is the inverse of farness, which is measured as the sum of distance to all other nodes within the network. CC specifies the time required for information to flow from the node to all other nodes and represents the closeness of a node to all other nodes in a network structure [40]…”

“…BC measures the number of times a node acts as the shortest (geodesic) pathways for a pair of vertices. High BC indicates a strong possibility to influence to control the flow of information within the network [40]…”