Models and Methods in Social Network Analysis - Essential 1990s

In this review of Models and Methods in Social Network Analysis, the reviewer finds a focused, methodologically rich companion for researchers who need a rigorous survey of network techniques developed in the 1990s. The volume is best for graduate students, methodologists, and applied social scientists who want concise, expert treatments of topics such as network measurement, sampling, centrality analysis, blockmodelling, diffusion, two-mode networks, and random graph theory. The single biggest reason to buy is its clear review of substantive quantitative advances led by respected authors in the field.

Key Features

• Comprehensive reviews: Each chapter summarizes the most significant quantitative developments in social network analysis from the 1990s, giving readers a reliable survey of that decade's progress.
• Methodological depth: The authors walk through measurement and sampling advances so researchers can evaluate and apply rigorous data-handling approaches to network datasets.
• Centrality and positional analysis: Detailed discussion of centrality measures and blockmodelling helps analysts choose appropriate metrics for different research questions.
• Two-mode and diffusion coverage: The book treats affiliation networks and diffusion processes, useful for studying group ties and spread phenomena in networks.
• Theory of random graphs: A dedicated review of random graph theory provides theoretical grounding for probabilistic models used in empirical work.
• Authored by leading methodologists: Contributions from established scholars ensure the treatments are authoritative and well referenced for further reading.

Who It's For

This volume is ideal for graduate students in sociology or political science, doctoral researchers, and applied methodologists who need a compact but thorough account of late 20th-century developments in quantitative network methods. In particular, those designing network measurement strategies, sampling schemes, or selecting centrality and blockmodel techniques will find direct value.

Those seeking step-by-step software tutorials, elementary introductions for general readers, or an up-to-the-minute survey of methods after the 1990s should look elsewhere; the book assumes some familiarity with social network concepts and focuses on methodological review rather than introductory pedagogy.

Pros & Cons

Pros

• Authoritative synthesis by leading scholars provides a trustworthy overview of key methodological advances.
• Wide topical range covers measurement, sampling, centrality, blockmodelling, diffusion, two-mode networks, and random graphs in one volume.
• Useful for designing rigorous empirical work because it emphasizes quantitative models and their assumptions.

Cons

• Not a beginner textbook; readers without prior network background may find some discussions terse.

Specifications

Title	Models and Methods in Social Network Analysis
Series	Structural Analysis in the Social Sciences, Series Number 28
Authors	Peter J. Carrington, John Scott, Stanley Wasserman
Scope	Quantitative models and methods from the 1990s
Topics covered	Measurement, sampling, centrality, blockmodelling, diffusion, two-mode networks, random graphs
Use case	Graduate-level methods reference and survey

Our Verdict

Models and Methods in Social Network Analysis is a compact, expert-led review that belongs on the shelf of any researcher working seriously with network data from a methodological perspective. It delivers concentrated, well-organized surveys of core quantitative topics from the 1990s and is good value for students and scholars who need authoritative synthesis rather than introductory instruction.

Frequently Asked Questions

Is this book suitable for beginners?
The book assumes some prior knowledge of social network concepts and is best for readers with at least an introductory background in network analysis.

Does it cover empirical applications or software?
The volume focuses on methodological advances and theory rather than step-by-step software tutorials or extensive empirical casework.

Which topics are emphasized?
Key emphases include network measurement, sampling, centrality, blockmodelling, diffusion, affiliation networks, and random graph theory.