Social Network Analysis: A Guide Using Principles of the ADAPT Model

Preface

Welcome! So you want to know a little bit more about social network data? Good. This book is designed to strip away all of the theory, the jargon and the fluff and just build skills in Social Network Analysis. In this, we cover fundamental principles of working with social network data that will provide you not only recipes of code to implement, but a workflow to follow with your project and practice “assignments” to apply those skills. This is designed this using principles from the data science teaching and learning model called ADAPT (more on the acronym later). This model provides a framework for learning that are based on principles of ethical data science. This book, therefore, demonstrates principles of the model and discusses aspects from it as they pertain to each topic (learn more in Chapter 2).

By the end of this you will have learned the fundamental principles of working with network data. The aim of this book is really to demonstrate to novice data science enthusiasts that working with network data fits in very well with typical workflows that you are already learning. It situates network data as another gem mine for you to work with and another tool for you to use as you grow your skills in working with diverse types of data.

You will find that the book is divided into three sections that mirror the units of a course. Each section of the book takes you through a paired down stage of a typical data science workflow that provides you with the building blocks of this approach. First, it starts building your skills wrangling (cleaning and transforming) network data. In this unit you will be learning about network data structured, bringing network data into R and best practices for cleaning network data. Second, it transitions into a unit on network visualisation. This unit builds your skills in basic, intermediate, and advanced visualisation. The aim of this unit is to help you create clean network visualisations that tell a clear story and engage your viewers. Finally, the book finishes with some modules on analysing network data. The aim of this unit is to take you through some very commonly used metrics in network analysis. Since the scope of this book is more on data science and less on network theory or mathematics, this unit details conceptually what the measures do and what you can do with them.

There is much to learn beyond this book. We cannot cover every single technique, best practice or analytic tool. Epecially in an introductory handbook. There are so many other ways to accomplish what we do in this book (like every data science project!) and even more ways you can work with network data. This simply gets you going on your path. In the conclusion I introduce you to a few other resources that can aide you in more advanced tools or more theory forward discussion on networks and network analysis. This content, however, is plenty to get you going and to help you wow folks with some cool looking visuals and some useful analysis.

Enjoy!

Acknowledgements

This project would not have been possible without funding from the National Science Foundation -2222148 . Furthermore, I could not have done it without the endless tutelage from my good friends and mentors Andrew Davis and Steve McDonald. Good to acknowledge my personal network who first introduced me to network analysis! I must also thank the reviewers for their constructive input and for catching my vision for this book. Finally, my dear little family for all their love and support. Especially, Lexi, thanks for putting up with my relentless pestering for her opinion on these network visuals!