Same But Different. A Comparison of Estimation Approaches for Exponential Random Graph Models for Multiple Networks.
- Author(s)
- Petro Tolochko, Hajo Boomgaarden
- Abstract
The Exponential Random Graph family of models (ERGM) is a powerful tool for social science research as it allows for the simultaneous modeling of endogenous network characteristics and exogenous variables such as gender, age, and socioeconomic status. However, a major limitation of ERGM is that it is mainly used for descriptive analysis of a single network. This paper examines two methods for estimating multiple networks: hierarchical and integrated. We contrast the two approaches, evaluate their accuracy and discuss the advantages and drawbacks of each. Furthermore, we make recommendations for future researchers on how to proceed with multiple network analysis depending on various factors such as the number of networks and the hierarchical structure of the data. This research is important as it highlights the need for the analysis of multiple networks in order to gain a more comprehensive understanding of social phenomena and the potential for new discoveries.
- Organisation(s)
- Department of Communication
- Journal
- Social Networks
- Volume
- 76
- Pages
- 1-11
- ISSN
- 0378-8733
- DOI
- https://doi.org/10.1016/j.socnet.2023.05.003.
- Publication date
- 01-2023
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 508008 Media analysis
- Keywords
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/same-but-different-a-comparison-of-estimation-approaches-for-exponential-random-graph-models-for-multiple-networks(2706c99f-1e5b-4709-92fd-c7b35da0d0f2).html