To build a network and find the most influential data science twitter uses, we will use the NetworkX2 package to create a directed graph and to calculate eigenvector centrality (a measure of network influence) among the nodes (twitter users)…
Nodes represent twitter handles and the edges between the nodes represent user mentions. The size and color of the nodes correspond to eigenvector centrality values, which, again, is one measure of network influence. Let’s take a quick peek at the top 10 influencers (who are also plotted above):
The top 10 influencers include some of the most respected individuals and organizations in data science, and so their influence among data scientists on twitter is not at all surprising.