The measure known as closeness centrality is one of the oldest centrality measures used in network science, proposed by the sociologist, Alex Bavelas, in 1950. Closeness is defined as the reciprocal of farness. What is farness? It's the reciprocal of closeness, of course! More helpfully, the farness of a node is the sum of distances between that node and all other nodes. So, a node with high closeness centrality is literally close to other nodes. Nodes with high closeness have, on average, short paths to many other nodes, which can be helpful for disseminating resources quickly.

The following example uses the NetworkX closeness_centrality() function to calculate the closeness centrality values for the suffragette network and display the top 10:

closeness = nx.closeness_centrality(G)
sorted(closeness.items(), key=lambda x:x[1], reverse=True)[0:10]

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