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A survey on exponential random graph models: an application perspective
Saeid Ghafouri1, Seyed Hossein Khasteh1
1School of computer engineering, K. N. Toosi University of Technology, Tehran, Iran.
Peerj. Computer Science
|April 5, 2021
Summary
Exponential Random Graph Models (ERGM) offer statistical insights into complex networked data. This review explores ERGM methods and their diverse applications across various scientific fields.
Area of Science:
- Network analysis
- Statistical modeling
- Graph theory
Background:
- Real-world phenomena often exhibit uncertainty, leading to increased interest in statistical analysis.
- Modeling problems as networks is a common approach, highlighting the importance of statistical network analysis.
- Exponential Random Graph Models (ERGM) are a popular statistical method for analyzing networked data.
Purpose of the Study:
- To provide a comprehensive review of Exponential Random Graph Models (ERGM).
- To explore classic and newly presented ERGM approaches and research.
- To conduct a comprehensive study on the applications of ERGMs across various research areas.
Main Methods:
- Review of foundational concepts and classic ERGM methodologies.
- Analysis of recent advancements and novel approaches in ERGM.
- Compilation and categorization of ERGM applications in diverse scientific domains.
Main Results:
- Detailed explanation of ERGM building blocks and traditional methods.
- Overview of emerging ERGM techniques and contemporary research.
- Extensive documentation of ERGM applications, offering a novel contribution to the field.
Conclusions:
- ERGMs are powerful tools for understanding network structures and dynamics.
- This review serves as an introductory guide for researchers utilizing ERGMs.
- The comprehensive application survey aims to facilitate ERGM adoption in new disciplines.
Keywords:
ERGMERGMs’ applicationsERGMs’ surveyExponential random graph models surveyExponential random graphsMore Related Videos
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