Handbook of Graphs and Networks in People Analytics: With Examples in R and Python covers the theory and practical implementation of graph methods in R and Python for the analysis of people and organizational networks. Starting with an overview of the origins of graph theory and its current applications in the social sciences, the book proceeds to give in-depth technical instruction on how to construct and store graphs from data, how to visualize those graphs compellingly and how to convert common data structures into graph-friendly form.
The book explores critical elements of network analysis in detail, including the measurement of distance and centrality, the detection of communities and cliques, and the analysis of assortativity and similarity. An extension chapter offers an introduction to graph database technologies. Real data sets from various research contexts are used for both instruction and for end of chapter practice exercises and a final chapter contains data sets and exercises ideal for larger personal or group projects of varying difficulty level.
- Immediately implementable code, with extensive and varied illustrations of graph variants and layouts
- Examples and exercises across a variety of real-life contexts including business, politics, education, social media and crime investigation
- Dedicated chapter on graph visualization methods
- Practical walkthroughs of common methodological uses: finding influential actors in groups, discovering hidden community structures, facilitating diverse interaction in organizations, detecting political alignment, determining what influences connection and attachment
- Various downloadable data sets for use both in class and individual learning projects
- Final chapter dedicated to individual or group project examples
Table of Contents
1. Graphs Everywhere!, 2. Working With Graphs, 3. Visualizing Graphs, 4. Restructuring Data For Use in Graphs, 5. Paths and Distance, 6. Vertex Importance and Centrality, 7. Components, Communities and Cliques, 8. Assortativity and Similarity, 9. Graphs as Databases, 10. Further Exercises for Practice
Keith McNulty, PhD is a leading practitioner of applied mathematics, statistics, psychometrics and people analytics. He is currently Global Director of Talent Science and Analytics at McKinsey & Company.
“It is exciting and inspiring to see the way McNulty explains network methods, as he unpacks the distinct elements and analytic steps to make them transparent. This makes it easier for readers to see how these elements fit together and apply to organizational challenges, sparking new ideas for innovative solutions. By demystifying this topic, McNulty empowers people to find their own solutions and engage in more productive conversations, regardless of who is writing the actual code or running the analyses. This book can help to democratize network analysis and improve the level of data fluency in organizations more generally.”
- From the foreword by Professor Jeff Polzer, Harvard Business School