9  Introduction

Network visualization is a powerful tool for exploring and communicating the structure of relational data. While many software packages can produce aesthetically appealing network graphs, effective visualization requires more than visual appeal. It demands a deep understanding of layout algorithms, visual encoding strategies, and the specific characteristics of the network being studied.

R offers a rich ecosystem of packages for network visualization, with ggraph emerging as a particularly flexible and powerful option. Built on top of ggplot2, ggraph brings the grammar of graphics to network visualization, allowing for fine-grained control over every aspect of a plot while maintaining a consistent and intuitive syntax.

In this part, we explore the fundamentals of network visualization in R, with a primary focus on ggraph. We examine different layout algorithms which determine how nodes are positioned in space. We also discuss how to effectively encode node and edge attributes using visual properties such as size, color, and shape.

Note that igraph, while primarily a network analysis package, also provides powerful plotting capabilities. However, the way plots are constructed fundamentally differs from ggraph. In igraph, plotting is more procedural, where you directly specify the layout and visual properties in a single function call. In contrast, ggraph follows a layered approach, allowing you to build up your plot incrementally, which can lead to more complex and customized visualizations.