Rectangling (Social) Network Data, Advanced Options

Link features, for link prediction

This walkthrough is a follow up on my previous post about rectangling network data As a recap: we want to predict links between nodes in a graph by using features of the vertices. In the previous post I showed how to load flat files into a graph structure with {tidygraph}, how to select positive and negative examples, and I extracted some node features. Because we want to predict if a link between two nodes is probable, we can use the node features, but there is also some other information about the edges in the graph that we cannot get out with node features only procedure. [Read More]

Rectangling (Social) Network Data

Preparing data for link prediction

In this tutorial I will show you how we go from network data to a rectangular format that is suited for machine learning. Many things in the world are graphs (networks). For instance: real-life friendships, business interactions, links between websites and (digital) social networks. I find graphs (the formal name for networks) fascinating, and because I am also interested in machine learning and data engineering, the question naturally becomes: How do I get (social) network data into a rectangular structure for ML? [Read More]