An Introduction to Exponential Random Graph Modeling

An Introduction to Exponential Random Graph Modeling

Author: Jenine K. Harris

Publisher: SAGE Publications

Published: 2013-12-23

Total Pages: 138

ISBN-13: 148332205X

DOWNLOAD EBOOK

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.


An Introduction to Exponential Random Graph Modeling

An Introduction to Exponential Random Graph Modeling

Author: Jenine K. Harris

Publisher: SAGE Publications

Published: 2013-12-23

Total Pages: 138

ISBN-13: 1483303438

DOWNLOAD EBOOK

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. An Introduction to Exponential Random Graph Modeling, by Jenine K. Harris, 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.


Inferential Network Analysis

Inferential Network Analysis

Author: Skyler J. Cranmer

Publisher: Cambridge University Press

Published: 2020-11-19

Total Pages: 317

ISBN-13: 1107158125

DOWNLOAD EBOOK

Pioneering introduction of unprecedented breadth and scope to inferential and statistical methods for network analysis.


Animal Social Networks

Animal Social Networks

Author: Dr. Jens Krause

Publisher: Oxford University Press, USA

Published: 2015

Total Pages: 279

ISBN-13: 0199679053

DOWNLOAD EBOOK

This book demonstrates the application of network theory to the social organization of animals.


Random Graphs and Complex Networks

Random Graphs and Complex Networks

Author: Remco van der Hofstad

Publisher: Cambridge University Press

Published: 2017

Total Pages: 341

ISBN-13: 110717287X

DOWNLOAD EBOOK

This classroom-tested text is the definitive introduction to the mathematics of network science, featuring examples and numerous exercises.


A Survey of Statistical Network Models

A Survey of Statistical Network Models

Author: Anna Goldenberg

Publisher: Now Publishers Inc

Published: 2010

Total Pages: 118

ISBN-13: 1601983204

DOWNLOAD EBOOK

Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.


Statistical Modelling by Exponential Families

Statistical Modelling by Exponential Families

Author: Rolf Sundberg

Publisher: Cambridge University Press

Published: 2019-08-29

Total Pages: 297

ISBN-13: 1108476597

DOWNLOAD EBOOK

A readable, digestible introduction to essential theory and wealth of applications, with a vast set of examples and numerous exercises.


Introduction to Random Graphs

Introduction to Random Graphs

Author: Alan Frieze

Publisher: Cambridge University Press

Published: 2016

Total Pages: 483

ISBN-13: 1107118506

DOWNLOAD EBOOK

The text covers random graphs from the basic to the advanced, including numerous exercises and recommendations for further reading.