Network centrality measures
Degree centrality
Degree centrality[1] is the number of links that connect the node to the network divided by (the number of nodes in the network minus 1). It is a local measure that does not account for network context. However, changes in node with high degree centrality are likely to influence a large number of nodes in the network.Closeness centrality
Closeness centrality[2] is equal to the inverse to the average of the shortest paths between this node and all other nodes in the network. Closeness centrality characterizes how close the node is to all the other nodes. The information originating at the node with the smallest value of closeness centrality needs the minimum number of steps to propagate in the network.Betweenness centrality
Betweenness centrality[3] equals to the ratio of the number of shortest paths going through the node to the total number of shortest paths existing in the network: It shows how influential the node is over all the information flows in the network.Clustering coefficient
Clustering coefficient[4] of a vertex in a graph quantifies how close the vertex and its neighbors are from being a clique (complete graph).References
- Scott JP. Social Network Analysis: A Handbook. Sage Publications, 2nd edition, 2000.
- Wasserman S, Faust K. Social Network Analysis: Methods and Application, Cambridge University Press, United Kingdom, 1994.
- Freeman LC. A Set of Measures of Centrality Based on Betweenness. Sociometry 1977; 40:35-41.
- D. J. Watts and Steven Strogatz (June 1998). "Collective dynamics of 'small-world' networks". Nature 393: 440–442.
Last modified: June 05, 2008