This repository hosts the code for the following tutorials on Medium.
Learn the basics of Social Network Analysis with Python and NetworkX by exploring Billy Corgan's sphere of influence among musicians.
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Visualizing Social Networks for Better Insights: Analyzing and Mapping Social Relationships with Python’s NetworkX Library
In Part 1, we explore a special kind of link analysis called Social Network Analysis (SNA). We can conduct Social Network Analysis with Python using the library NetworkX. First, we introduce Python and NetworkX. Then we explore the basics of SNA, such as nodes, edges, and measures of centrality. -
Visualizing Social Networks for Better Insights: Analyzing and Mapping Social Relationships with Python’s NetworkX Library — Part 2
In the previous installment, we kept the network small and simple. In Part 2, we will continue to use Python and NetworkX to examine Billy Corgan’s sphere of influence. We will also expand Billy Corgan’s network to make it more complex and increase our understanding of degree centrality and betweenness centrality. As we work through this example, we will discuss the context and how domain knowledge is essential to maximizing the benefits of social network analysis.
- Closeness and Communities: Analyzing Social Networks with Python and NetworkX — Part 3 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.
- Graphing Billy Corgan’s Network: Analyzing and Mapping Social Relationships with Python’s NetworkX Library — Part 4
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 who were members of the bands Zwan and Smashing Pumpkins, we made inferences about the relationships between the members.
