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Handbook of Graphs and Networks in People Analytics

A practical handbook teaching the theory and applied methods of graph and network analysis for studying people, groups and organizations, with worked examples in R and Python.

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What it’s about

Most of us live inside huge graphs—social networks, family trees, communication systems—yet few people know how to analyze the network structures that shape connection, influence and information flow in organizations. This handbook by Keith McNulty demystifies graph and network analysis for students and practitioners in the social, organizational and people-analytics fields, explaining just enough theory to support analytical curiosity while teaching the concrete, reproducible methods needed to create, visualize and analyze graphs using freely available open-source tools. From building graphs out of messy rectangular data, to computing paths, distance, centrality, communities and cliques, to persisting data in graph databases, the book grounds every concept in real example datasets and runnable code. Readers finish able to apply network thinking to organizational problems such as onboarding new hires, encouraging diverse collaboration, finding influential employees, detecting communities, and identifying superconnectors—without expensive proprietary software.

The through-line

Who it’s for
A technical practitioner, analyst, student or researcher who wants to understand and analyze connections among people, groups and organizations.
The problem
Their data is stored in transactional/rectangular form and they lack the skills to model, visualize and analyze relationships as networks. They feel that network analysis is an esoteric 'dark art' beyond their reach and worry they need expensive specialist software.
The plan
  1. Learn elementary graph theory and how to create graph objects from data.
  2. Visualize graphs effectively using layouts and styling.
  3. Restructure existing data into graph-friendly edge and vertex sets.
  4. Measure paths, distance, centrality, communities and cliques.
  5. Persist data in graph databases for repeatable analysis.
The payoff
The reader can confidently build, visualize and analyze networks to solve organizational problems like onboarding, diverse collaboration and finding influencers. · They can democratize network analysis in their organization and have more productive, data-fluent conversations. · They no longer need expensive vendor software to derive rigorous network insights.

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