With the increasing availability of granular data on the relationships between individual entities - such as persons (social media), countries (international trade) and financial institutions (supervisory reporting) - network analysis offers many possibilities to extract useful information from such data. This post provides an introduction to network analysis in R using the powerful igraph package for the calculation of metrics and ggraph for visualisation. It marks the beginning of a more comprehensive treatment of network analysis on r-econometrics.
There are multiple packages for the analysis of networks in R. This page concentrates on the igraph package, which allows for a broad range of applications. But before we get into it in more detail, it is useful to know that there are two possible ways to represent the edges, i.e. the connections, of a network:
We can illustrate these two representations by looking at an artificial network. Such a network could be generated with certain functions of the igraph package. However, the following code only uses base functionalities of R. It results in a data frame with the names of the connected entities in the first and second row. The third row contains a random indicator for the strength of the connection. It is based on the square of a value from a standard normal distribution. To reduce the number of resulting edges, only values above a certain threshold are kept. Also, the code excludes the connections a node has with itself.
With the increasing availability of granular data on the relationships between individual entities - such as persons (social media), countries (international trade) and financial institutions (supervisory reporting) - network analysis offers many possibilities to extract useful information from such data. This section provides brief introductions to the analysis of network data in R.
If a network is small, it can be easily summarised by its graph or a figure. But once a network reaches a certain size, it becomes more meaningful to use more formal summary statistics in order to describe its features. This post covers some basic network summary statistics as presented in Jackson (2008). The metrics are based on the concept of centrality, which describes the importance of a node in a given network of nodes.
Beside the calculation of summarising network metrics, the visualisation of a graph can also be a very informative step in network analysis. Since visualisations in R usually involve the ggplot2 package, I focus on the ggraph package, which is based on the ggplot2 architecture. For illustration I use the artificial data set from my post on network analysis, which is an igraph object names graph_df.
When using ggplot2 the main challenge in the visualisation of networks is to find suitable x- and y-coordinates for the nodes of a graph. Fortunately, there are some packages out there, which were developed for this purpose. For example, ggraph and ggnetwork take an igraph object and produce tables with suitable coordianates. These coordinates are usually obtained by using special algorithms such as the ones proposed by Fruchterman and Reingold (1991) or Kamada and Kawai (1989). The latter is applied in the code below.