Same But Different. A Comparison of Estimation Approaches for Exponential Random Graph Models for Multiple Networks.

Autor(en)

Petro Tolochko, Hajo Boomgaarden

Abstrakt

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(en)

Institut für Publizistik- und Kommunikationswissenschaft

Journal

Social Networks

Band

76

Seiten

1-11

ISSN

0378-8733

DOI

https://doi.org/10.1016/j.socnet.2023.05.003.

Publikationsdatum

01-2023

Peer-reviewed

Ja

ÖFOS 2012

508008 Medienanalyse

Schlagwörter

Link zum Portal

https://ucris.univie.ac.at/portal/de/publications/same-but-different-a-comparison-of-estimation-approaches-for-exponential-random-graph-models-for-multiple-networks(2706c99f-1e5b-4709-92fd-c7b35da0d0f2).html