Proceed learning the best way to conduct social network evaluation with NetworkX and Python

In the beginning of our investigation into Billy Corgan’s sphere of influence, we introduced social network evaluation and basic concepts like nodes and edges. In Part 2, we expanded our understanding of social network evaluation 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 analyze the relationships between the members of different bands. At the identical time, we discussed how domain knowledge helps to tell our understanding of the outcomes.

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

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

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

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