Graphs are an excellent way of showing high-dimensional data in an intuitive way. But when it comes to representing graphs as matrices, it can be a little less intuitive. Earlier, we looked at how to represent an undirected graph as an adjacency matrix. In this tutorial, we'll be looking at ... READ the POST
Node customization for stunning networks
As we saw earlier, network visualization in R is a breeze with the visNetwork package. The graphs are gorgeous, interactive, and fun to play with. In this article, we'll look at how we can customize the nodes of our network to convey additional information. First, we'll learn how to color a network ... READ the POST
Giving your networks a user-friendly makeover
When it comes to network analysis, igraph is a beast of a package with extensive documentation. If you do network analysis in R, chances are you've at least heard of it. With igraph, you can create and compare deterministic or stochastic networks, calculate centrality measures, and find communities ... READ the POST
How to create co-occurrence networks with the R packages ‘cooccur’ and ‘visNetwork’
Co-occurrence networks are a graphical representation of how frequently variables appear together. They're commonly used in ecology and text mining, where co-occurrence measures how frequently two species are seen together within a sampling site or how frequently two words are present in a single ... READ the POST
How to Represent an Undirected Graph as an Adjacency Matrix
Graphs are an excellent way to gain a deeper understanding of large systems of information as they provide us a flexible and intuitive way to generate insight through visualizing the relationships within the data. In this tutorial, we'll focus specifically on undirected graphs. Both Facebook and ... READ the POST