R Tutorial : Network analysis in R: A tidy approach

Want to learn more? Take the full course at https://learn.datacamp.com/courses/network-science-in-r-a-tidy-approach at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Welcome to this course on network analysis in R. My name is Massimo Franceschet and I teach data and network science. This course will teach you how to analyze and visualize network data in the R environment using the tidy approach. Social networks such as Facebook and Twitter are popular examples of networks. Other examples include the Internet, the Web, academic citation networks like Google Scholar, and even the neural network in your brain. A network, or graph, is made of a set of nodes, or vertices, and a set of ties, also called edges, that connect pairs of nodes. In the figure, you see a network representing a website. The circles are the nodes, the webpages in this case, and the lines connecting the circles are the ties, the links between web pages. Here you see a directed network with six nodes and eight directed ties. In a directed network, ties have a direction, that is, there are a source and a target node. An example is Twitter, where there are followers and followees. Here, Bob follows Emily, but Emily does not follow him. David and Emily follow each other. This is an undirected network with six nodes and seven undirected ties. In an undirected network, ties don't have a particular direction. Facebook networks are undirected since friendship is a mutual relationship. Here, Emily and Alice are friends, also Alice and Bob are friends, but Emily and Bob are not friends. Networks can also be weighted. In this case, the ties between nodes have weights assigned to them. On the Internet, tie weights might represent the connection bandwidth between routers. On Facebook, tie weights might correspond to the duration of a friendship. In the Facebook network here, Alice and Emily have been friends for two years, while Charles and Dav