We are very pleased to present “Social Network Analysis with Applications.” This book will focus on models and methods for social network analysis applied to organizational risk. Current books in the area of social network analysis are both highly technical and written at the advanced graduate level or they only discuss general concepts and omit mathematical calculations. Very few of the texts offer any practice problems for practitioners and students to complete as practice problems or homework exercises.
The inclusion of mathematical calculations is central to our approach in this text. Many in the field prefer to present social network analysis by hiding the mathematics and relying on computer software to identify centrality. We contend that this is a critical mistake in the pedagogy of network analysis. The authors have taught over 30 courses in social network analysis to over 500 students with varied approaches and consulted with many more colleagues. Those who have learned how to calculate centrality measures by hand calculation, for example, are 11 times more likely to retain an understanding of what the measures mean 1–3 months after the course. Thus, it is not our contention that an individual would use hand calculations on any real-world example. However, in learning to calculate measures by hand, the mathematics leads to an understanding of the underlying principles of social network analysis.
Many practitioners in industry, management, military intelligence, and law enforcement have expressed a growing interest in social network analysis, specifically focused on identifying organizational risk. We operationally define organizational risk as vulnerability in the social network. This could be a node high in informal power or a rare broker of resources. This could be a point of influence for the diffusion of ideology. There may exist many networks within an organization, such as a friendship network, a resource network, or a knowledge network. One or more of these networks may present organizational risk, while the others do not. In a military or law enforcement application, organizational risk identifies targets for further development and investigation. In an industry or management application, organizational risk identifies informal power brokers that should be included in management decisions, and potential vulnerability from lack of redundancy. The authors attempt to present examples of both.
The authors have trained soldiers in Iraq and Afghanistan, local and national police, and industry professionals on the applications of social network analysis. The topics laid out in this book follow the curriculum that they have used for the past 7 years in teaching week-long workshops as well as graduate and undergraduate level college courses.
The first three chapters introduce the mathematical concepts of a network and centrality. Again, we contend that an understanding of the mathematics leads to a deeper and more complete understanding of the social concepts behind social network analysis. Social network analysis software is also presented for larger problems. Visual analysis methods are also introduced. At the conclusion of the first three chapters, the reader should have a basic understanding of common network analysis techniques.
Chapters 5–7 provide the social theory that underlies social network analysis. It is infeasible to include all social theory related to social networks in the space afforded in this text. Therefore, the social theory that is most applicable to understanding organizational risk is provided. The authors' selection of the material included in these sections comes from experience applying social network analysis in industry, counter terrorism, and law enforcement. We have also sought input from former students who are actively using social network analysis on a regular basis. The reader is reminded that the focus of this text is for practitioners intending to apply social network analysis to organizations.
Chapters 7 and 8 are directed toward data. Matrix algebra is included in an appendix and provides a primer for the necessary mathematics to understand meta-networks and relational algebra. Relational algebra is an often overlooked method. While there is limited application for single mode networks, it is a critical tool for handling meta-networks, also known as multiplex or multimode networks. Relational algebra is the means to transform any relational data into different social networks, each of which might reveal organizational risk. Chapter 8 reviews sources of data. Strengths and weaknesses of data sources are presented. At the conclusion of Chapter 8, the reader should be capable of collecting network data and identifying organizational risk. The final chapter provides an organized approach to applying the methods and techniques presented in the book. It is intended to serve as a review, leading the reader through examples of applied social network analysis.
We are confident that you will enjoy “Social Network Analysis with Applications,” and feel empowered by the time you are finished. We have been amazed at the innovative and interesting ways our students have applied social network analysis to a wide variety of problems. More are sure to follow. Welcome to the fastest growing field in science!
IAN A. MCCULLOH
Curtin University, Australia
September, 2013