Network Graphs with NetworkX and Matplotlib

Continue learning how to conduct social network analysis with NetworkX and Python

At the beginning of our investigation into Billy Corgan’s sphere of influence, we introduced social network analysis and basic concepts like nodes and edges. In Part 2, we expanded our understanding of social network analysis by graphing the relationships between the members of the bands Smashing Pumpkins and Zwan. Then, we examined metrics like degree centrality and betweenness centrality to investigate the relationships between the members of the different bands. At the same time, we discussed how domain knowledge helps to inform our understanding of the results.

In Part 3, we introduced a third centrality measure, closeness centrality. We also began a discussion on the concept of communities and subgroups and demonstrated different community graphs and how we might use closeness centrality to inform our interpretation. Using the network of musicians that were members of the bands Zwan and Smashing Pumpkins, we made inferences about the relationships between the members.

This time around, we will make our results more interesting by expanding the network and adding additional bands. At the same time, we will expand our understanding of measures of centrality and the concept of community while refining our Matplotlib skills to make your NetworkX graphs even more engaging.

The band tool performing on stage in 2022. Found on Wikimedia Commons ( — posted by user Lugnuts.
Tool — By Lugnuts — Own work, CC BY-SA 4.0

In previous installments, we covered three essential metrics in social network analysis: degree centrality, betweenness centrality, and closeness centrality. We also discussed the concept of communities and described how that framework can be applied to understand network dynamics among the communities/bands that comprise Billy Corgan’s network.

A NetworkX graph created with Matplotlib. The nodes are members of the Smashing Pumpkins and Zwan.
Remember this from last time? It’s a NetworkX graph we created with Matplotlib.

While even a small group of musicians can exhibit interesting network dynamics, the lack of complexity in the network made our results less…

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