You are here

SAGE online ordering services and account tools will be unavailable due to system maintenance between 1am and 3pm BST, Saturday 19th September. If you need assistance, please contact SAGE at . Thank you for your patience and we apologise for the inconvenience.

Resources to help you transition to teaching online

Instructors: To support your transition to online learning, please see our resources and tools page whether you are teaching in the UK, or teaching outside of the UK.

Inspection copy update April 2020: Due to the current restrictions in place in response to COVID-19, our inspection copy policy has changed. Please refer to our updated inspection copy policy for full details. If you have recently placed an inspection copy order with us, we will be in touch to advise of any changes.

An Introduction to Exponential Random Graph Modeling

An Introduction to Exponential Random Graph Modeling

Additional resources:

February 2014 | 136 pages | SAGE Publications, Inc
This volume introduces the basic concepts of Exponential Random Graph Modeling (ERGM), gives examples of why it is used, and shows the reader how to conduct basic ERGM analyses in their own research. ERGM is a statistical approach to modeling social network structure that goes beyond the descriptive methods conventionally used in social network analysis. Although it was developed to handle the inherent non-independence of network data, the results of ERGM are interpreted in similar ways to logistic regression, making this a very useful method for examining social systems. Recent advances in statistical software have helped make ERGM accessible to social scientists, but a concise guide to using ERGM has been lacking. This book fills that gap, by using examples from public health, and walking the reader through the process of ERGM model-building using R statistical software and the statnet package.

An Introduction to Exponential Random Graph Modeling is a part of SAGE’s Quantitative Applications in the Social Sciences (QASS) series, which has helped countless students, instructors, and researchers learn cutting-edge quantitative techniques. Learn more about the QASS series here.
1. The Promise and Challenge of Network Approaches
2. Statistical Network Models
3. Building a Useful Exponential Random Graph Model
4. Extensions of the Basic Model for Directed Networks and Using Dyadic Attributes as Predictors
5. Conclusion and Recommendations



Click the "Preview" tab above to download: 

  • Appendix A: R Commands
  • Appendix B: Modifying R-ergm Model Summary Procedure Using Fix()

Preview this book

Sample Materials & Chapters

Appendices: R Code

Chapter 3

For instructors

Select a Purchasing Option

SAGE Knowledge is the ultimate social sciences digital library for students, researchers, and faculty. Hosting more than 4,400 titles, it includes an expansive range of SAGE eBook and eReference content, including scholarly monographs, reference works, handbooks, series, professional development titles, and more.

The platform allows researchers to cross-search and seamlessly access a wide breadth of must-have SAGE book and reference content from one source.

SAGE Knowledge brings together high-quality content from across our imprints, including CQ Press and Corwin titles.