The increasing availability of dynamically changing digital data that can be used for extracting social networks over time has led to an upsurge of interest in the analysis of dynamic social networks.

One key aspect of dynamic social network analysis is finding the central nodes in a network.

We will see how this measure is computed and how to use the library networkx in order to create a visualization of the network where the nodes with the highest **betweenness** are highlighted.**Betweenness** **centrality** of a node `v` is the sum of the fraction of all-pairs shortest paths that pass through `v`: ..math:: c_B(v) =\sum_ \frac where `V` is the set of nodes, `\sigma(s, t)` is the number of (more...) def betweenness_centrality(G, k=None, normalized=True, weight=None, endpoints=False, seed=None): r"""Compute the shortest-path **betweenness** **centrality** for nodes.math:: c_B(v) =\sum_ \frac where `V` is the set of nodes, `\sigma(s, t)` is the number of shortest `(s, t)`-paths, and `\sigma(s, t|v)` is the number of those paths passing through some node `v` other than `s, t`.If `s = t`, `\sigma(s, t) = 1`, and if `v \in `, `\sigma(s, t|v) = 0` [2]_.