Summary

In this chapter, we looked at graphs from social networks and how to do cluster analysis on them. We also looked at saving and loading models from scikit-learn by using the classification model we created in Chapter 6, Social Media Insight Using Naive Bayes.

We created a graph of friends from a social network, in this case Twitter. We then examined how similar two users were, based on their friends. Users with more friends in common were considered more similar, although we normalize this by considering the overall number of friends they have. This is a commonly used way to infer knowledge (such as age or general topic of discussion) based on similar users. We can use this logic for recommending users to others—if they follow user X and user Y is similar to user X, they will probably like user Y. This is, in many ways, similar to our transaction-led similarity of previous chapters.

The aim of this analysis was to recommend users, and our use of cluster analysis allowed us to find clusters...