Character Interaction Networks for George R. R. Martin’s “A Song of Ice and Fire” saga These networks were created by connecting two characters whenever their names (or nicknames) appeared within 15 words of one another in one of the books in “A Song of Ice and Fire.” The edge weight corresponds to the number of interactions. You can use this data to explore the dynamics of the Seven Kingdoms using network science techniques. For example, community detection finds coherent plotlines. Centrality measures uncover the multiple ways in which characters play important roles in the saga.

With tidygraph I set out to make it easier to get your data into a graph and perform common transformations on it, but the aim has expanded since its inception. The goal of tidygraph is to empower the user to formulate complex questions regarding relational data as simple steps, thus enabling them to retrieve insights directly from the data itself. The central idea this all boils down to is this: you don’t have to plot a network to understand it. While I absolutely love the field of network visualisation, it is in many ways overused in data science — especially when it comes to extracting knowledge from a network. Just as you don’t need a plot to tell you which car in a dataset is the fastest, you don’t need a plot to tell you which pair of friends are the closest. What you do need, instead of a plot, is a tool that allow you to formulate your question into a logic sequence of operations. For many people in the world of rectangular data, this tool is increasingly dplyr (and friends), and I do hope that tidygraph can take on the same role in the world of relational data. https://www.data-imaginist.com/2018/tidygraph-1-1-a-tidy-hope/

Underneath the hood of tidygraph lies the well-oiled machinery of igraph, ensuring efficient graph manipulation. Rather than keeping the node and edge data in a list and creating igraph objects on the fly when needed, tidygraph subclasses igraph with the tbl_graph class and simply exposes it in a tidy manner. This ensures that all your beloved algorithms that expects igraph objects still works with tbl_graph objects. Further, tidygraph is very careful not to override any of igraphs exports so the two packages can coexist quite happily. https://www.data-imaginist.com/2018/tidygraph-1-1-a-tidy-hope/

This version adds 19(!) new ways to define the notion of centrality along with a manual version where you can mix and match different distance measures and summation strategies opening up the world to even more centrality scores. All of this wealth of centrality comes from the netrankr package that provides a framework for defining and calculating centrality scores. If you use centrality measures somewhere in your analysis I cannot recommend the vignettes provided by netrankr enough as they provide a fundamental intuition about the nature of such measures and how they can/should be used. https://www.data-imaginist.com/2018/tidygraph-1-1-a-tidy-hope/