49 Network Analysis in R

Luckily, if you are hoping to start your own Network Analysis, there are many libraries and tutorials available to assist you. To start, I would suggest using the tidygraph R library. Tidygraph is a tidy framework built over the igraph framework. Meaning, rather than working with convoluted lists to keep track of nodes and edges and their relationship as you would using only the igraph library, tidygraph supports a more tabular framework, which is the standard in other network programming environments, such as NetworkX for python or Gephi for a GUI interface. A really succinct tutorial for working with the tidygraph library can be found here: https://www.data-imaginist.com/2017/introducing-tidygraph/. This tutorial should empower you with the ability to define and visualize your own network using the tidygraph library.

An advantage for using the tidygraph library is that, because it is built on top of the igraph framework, it also supports tidygraph and igraph network analysis functions, although the igraph functions are supported through a tidygraph wrapper. Meaning that the all of the analysis we went over earlier - centrality, density, community detection - and many other analytical methods are all natively supported and can all be conducted in a “tidy” workflow! While the previous tutorial also has sections on how to perform network analysis with the tidygraph library, another strong tutorial in this task written by Dr. David Garcia can be found here: https://dgarcia-eu.github.io/SocialDataScience/5_SocialNetworkPhenomena/057_Tidygraph2/tidygraph2.html. Following this tutorial should give you a strong understanding of how to work with the tidygraph api to derive these metrics our of your graph, and also how to incorporate the calculated metrics in the visualization of your graph as well!

Now that you’re well equipped with the knowledge and tools of network analysis, I hope you enjoy networking!