Preface

0.1 Why Read this Book

Network analysis is a crucial strategy in understanding the direction and strength of connections between people, places and ideas. Visual representation of networks can speed the discovery process in working with data. It has a wide variety of applications and has been used to study social media like twitter, (Grandjean 2016), dispersion of knowledge among inventors, (Brennecke and Rank 2017) and the transmission of disease (Emch et al. 2012) , to name just a few.

The imagination of researchers to adopt social network analysis to new paradigms have left academic veterans reluctant to define its terms narrowly. (Easley and Kleinberg 2012)

0.2 Purpose

The purpose of this book is to speed the conversion of a traditional dataframe to a network diagram with nodes and vertices. Some discusson of basic computations will be included, but formulas are omitted unless necessary for understanding. Network analysis is challenging partially due to its reliance on unfamiliar data structures.

0.3 Collection

What follows is admittedly not the most original or insightful work on networks. It is an attempt to collect tutorials from disparate packages, software and websites in a single place. Attribution will, of course, be given where known.

0.4 Prerequisites

A working knowledge of R is necessary including how to obtain and load packages, how to manipulate basic data structures like lists and dataframes and how to plot and save graphs in multiple formats. Methods and packages in the tidyverse are preferred where available and Rstudio is the development environment of choice.

0.5 Disclaimer

I have no education or background in statistics, informatics or network analysis. Reliance upon any representation within this publication should occur, if at all, only after verification from other reliable sources and in consultation with someone with a relevant background.

0.6 Acknowledgements

There were a number of helpful tutorials that deserve explicit mention and public acclaim. The authors’ differing perspectives gave context on how best to learn network analysis with igraph. They include:

Janpu Hou, Network Visualization by igraph

References

Brennecke, Julia, and Olaf Rank. 2017. “The Firm’s Knowledge Network and the Transfer of Advice Among Corporate InventorsA Multilevel Network Study.” Research Policy 46 (4): 768–83. https://doi.org/10.1016/j.respol.2017.02.002.

Easley, David, and Jon Kleinberg. 2012. “Networks, Crowds, and Markets: Reasoning About a Highly Connected World.” Significance 9: 43–44. https://doi.org/10.1017/CBO9780511761942.

Emch, Michael, Elisabeth D Root, Sophia Giebultowicz, Mohammad Ali, Carolina Perez-Heydrich, and Mohammad Yunus. 2012. “Integration of Spatial and Social Network Analysis in Disease Transmission Studies.” Annals of the Association of American Geographers. Association of American Geographers 105 (5): 1004–15. https://doi.org/10.1080/00045608.2012.671129.

Grandjean, Martin. 2016. “A Social Network Analysis of Twitter: Mapping the Digital Humanities Community.” Edited by Aaron Mauro. Cogent Arts & Humanities 3 (1). Cogent OA: 1171458. https://doi.org/10.1080/23311983.2016.1171458.