R Tutorial : Network visualization

Want to learn more? Take the full course at https://learn.datacamp.com/courses/network-analysis-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- There are many ways of visualizing social networks. There are many things one can change about network vertices and edges, such as their size and color or by adding text. There are also many different layouts that can be used to arrange vertices and edges. Many of these look very attractive whilst others, particularly if there are a large number of vertices and edges, look incredibly bad. The most effective network visualizations should immediately provide insight and understanding to the viewer. There are a number of simple dos and don'ts that will help you think about how best to depict your network visualizations. The most commonly adjusted features of vertices in network visualizations are size, labels, color and shape. You have already investigated how to change some of these in igraph in the previous exercises. Adjusting size is excellent for highlighting key or influential vertices. For instance, larger vertices maybe those that are more central with a higher number of interconnections. Adding labels can also help identify key vertices, although too much text on a network visualization can render it hard to read. Color and shape are particularly useful for communicating differences in categorical vertex attributes. For edges, in addition to altering the thickness of lines to represent edge weights, you can also change the color or line type to indicate the type of interconnectivity between vertices. These styles can be done separately or in conjunction with each other. The most important consideration is to ensure that they highlight those key pieces of information that you wish to communicate to the audience. Many different layout algorithms have been generated that assist with how best to layout vertices when creating network visuali