Supervised Machine Learning on Network Data
In previous chapters, we spent a lot of time exploring how to collect text data from the internet, transform it into network data, visualize networks, and analyze networks. We were able to use centralities and various network metrics for additional contextual awareness about individual nodes’ placement and influence in networks, and we used community detection algorithms to identify the various communities that exist in a network.
In this chapter, we are going to begin an exploration of how network data can be useful in machine learning (ML). As this is a data science and network science book, I expect that many readers will be familiar with ML, but I’ll give a very quick explanation.
This chapter is composed of the following sections:
- Introducing ML
- Beginning with ML
- Data preparation and feature engineering
- Selecting a model
- Preparing the data
- Training and validating the model
- Model insights...
