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> Download PDF Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications (Structural Analysis in the Social Sciences)From

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Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications (Structural Analysis in the Social Sciences)From

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Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications (Structural Analysis in the Social Sciences)From

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Exponential random graph models (ERGMs) are increasingly applied to observed network data and are central to understanding social structure and network processes. The chapters in this edited volume provide the theoretical and methodological underpinnings of ERGMs, including models for univariate, multivariate, bipartite, longitudinal, and social-influence type ERGMs. Each method is applied in individual case studies illustrating how social science theories may be examined empirically using ERGMs. The authors supply the reader with sufficient detail to specify ERGMs, fit them to data with any of the available software packages, and interpret the results.

  • Sales Rank: #198938 in Books
  • Brand: Brand: Cambridge University Press
  • Published on: 2012-11-19
  • Original language: English
  • Number of items: 1
  • Dimensions: 8.98" h x .79" w x 5.98" l, 1.05 pounds
  • Binding: Paperback
  • 360 pages
Features
  • Used Book in Good Condition

Review
"I've been waiting impatiently for this book and I was definitely not disappointed. Finally we have a sourcebook on ERGMs that is both comprehensive and comprehensible. Most of the chapters are written for quantitative researchers who are not statisticians. Many illustrative empirical applications are worked through. Software packages are discussed. For the researcher who is intrigued by the possibility of analyzing network data with an ERGM, or who is already trying to do so, this is an indispensable resource." - Peter Carrington, University of Waterloo

"This collection offers readers an intuitive understanding of ERGMs, followed by a formal explanation of their statistical underpinnings as well as a methodological cookbook based on current software. Next, network scholars at the forefront of advancing theoretical and methodological contributions present eight compelling empirical studies. These studies illustrate how ERGMs offer exciting opportunities to advance theoretical understandings of network phenomena at the intra-organizational, inter-organizational, and societal levels." - Noshir Contractor, Jane S. & William J. White Professor of Behavioral Sciences, Northwestern University

"p*, the exponential family of random graph distributions introduced by Frank and Strauss in 1986, has indeed become the best statistical model in network science. This edited volume is a must-have - Lusher, Koskinen, and Robins have put together a thorough compilation for both the p* novice and enthusiast. It is the handbook to own - and use!" - Stanley Wasserman, Indiana University

About the Author
Dr Dean Lusher is Lecturer in Sociology at Swinburne University of Technology. He works closely with leading methodologists to develop an intuitive understanding of exponential graph models, how they link to broader network theory, and how to fit them to real-life data. His research applications are directed at issues of social norms and social hierarchies.

Dr Johan Koskinen is Lecturer in Social Sciences at the University of Manchester. He is a statistician working with statistical modeling and inference. Focusing on social network data, Dr Koskinen deals with generative models for different types of structures, such as longitudinal network data, networks nested in multilevel structures, and multilevel networks classified by affiliations.

Garry Robins is Professor in the School of Psychological Sciences at the University of Melbourne. Robins is a mathematical psychologist whose research deals with quantitative and statistical models for social and relational systems. His research has won international awards from the Psychometric Society, the American Psychological Association, and the International Network for Social Network Analysis.

Most helpful customer reviews

3 of 3 people found the following review helpful.
The Most Comprehensive, Reliable Reference to the Topic
By grandstar
As a Ph.D. student studying network analysis, I have long been waiting for a comprehensive guide to ERGM like this. I believe, if you have ever attempted to employ ERGM in your research, you must have experienced much frustration when trying to understand the basic concepts of ERGM and its application to real data just through journal articles and a few of book chapters. Fortunately, before too late, I came to read this book and learned a lot. I got it on Monday and read it through on Tuesday. (I was indeed in need of this book). Although the book is not a perfect guide to the topic, it offers a "relatively" easy-to-understand explanation about the most advanced technique in social network analysis. Unlike other books on advanced topics, which mostly look like a patchwork-style collection of irrelevant chapters, this book is coherent throughout all the chapters in themes, structure, and terminology. One caution is needed: its readers should be familiar with intermediate level of social network analysis (A mathematically inclined mind is needed). A suggestion: if the book is equipped with detailed guides to PNet, it would be a perfect one. Although two PNet manuals are available online, they are not that useful. Conclusion: if you need to do ERGM, this book is a must.

0 of 0 people found the following review helpful.
Very clearly written book
By Christina
I bought this book as a Kindle version, and read most of it on a flight home. The authors took an incredibly amount of time trying to present very complicated,complex material in a very clear fashion. The empirical examples are very helpful. I plan to make use of the suite of Pnet applications as a result of reading this book.

0 of 0 people found the following review helpful.
Great Research Reference
By MDT
This book is an invaluable resource for understanding Exponential Random Graphs. The combination of figures, example applications, and robust reference list makes it a great starting point for those interested in the modeling technique. This was extremely helpful for my research progress.

See all 9 customer reviews...

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