R Tutorial: Labeled networks, Social influence

Want to learn more? Take the full course at https://learn.datacamp.com/courses/predictive-analytics-using-networked-data-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In the last video, you became familiar with labeled networks and using the network to predict labels of nodes when they are unknown. It is time to get started on your own labeled network. You will be working on a customer network with the objective of predicting churn, i.e. identifying the customers that are most likely to terminate their contract with the company. Let's take a look at the dataset again. Here you see the first few rows of the customer dataframe with the customer id together with their churn indicator. 1 means that the customer churned and 0 that the customer did not churn. You can also see the first lines of the customer edgelist that was used to construct the network, with customer ids in each of the two columns. On the right is the corresponding network with churn nodes colored red and non-churn nodes colored white. The goal of the exercises is to predict who is most likely to churn in the future, based on the current situation. By using the social network we are assuming that churn is a social phenomenon and that being connected to someone that churned implies an increased probability of also churning. We will also talk about churn influence, meaning that the churners in the network influence others to churn as well. If we assume that churners influence others to churn as well, node 393 would have the highest churn probability in this case. Let's start by looking at a simple network learning technique to infer labels. It is called the relational neighbor classifier and assigns a label based on the labels of neighboring nodes by assuming that linked nodes have a propensity to have the same label. We will demonstrate this using the network of data scientists and focus again on Cecilia. We see that she h