Section 1 – Quick Introduction to Network Analysis


Lesson 10 – Betweenness Centrality

Betweenness centrality is another important measure of the node’s influence within the whole network. While degree simply shows the number of connections the node has, betweenness centrality shows how often the node appears on the shortest path between any two randomly chosen nodes in a network. Thus, betweenness centrality is a much better measure of influence because it takes the whole network into account, not only the local connectivity that the node belongs to.

A node may have high degree but low betweenness centrality. This indicates that it’s well-connected within the cluster that it belongs to, but not so well connected to the rest of the nodes that belong to the other clusters within the network. Such nodes may have high local influence, but not globally over the whole network. 

Alternatively, other nodes may have low degree but high betweenness centrality. Such nodes may have fewer connections, but the connections they do have are linking different groups and clusters together, making such nodes influential across the whole network. In fact, many efficient networkers and politicians will often trade some degree for betweenness centrality as it dramatically reduces their load while maintaining their central position within the network. 

 Network influence dynamics

In the example above “Sean” is a node with a low betweenness centrality, while “You” has a high betweenness centrality. “You” is connected to all the different nodes within a group, except for Sean, while Sean is only connected to Mark. However, if Sean then makes links both to Sergey and to Larry in the first cluster, connects to Priscilla, and maintains his link to Mark , he will have higher betweenness centrality than You, because he connects all the different groups that exist within the network, even though You has more connections than Sean.

In network visualization we often range the node sizes by their degree or betweenness centrality to indicate the most influential nodes, as shown above.