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A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model

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  • Leifeld, Philip
  • Cranmer, Skyler J.

Abstract

The temporal exponential random graph model (TERGM) and the stochastic actor-oriented model (SAOM, e.g., SIENA) are popular models for longitudinal network analysis. We compare these models theoretically, via simulation, and through a real-data example in order to assess their relative strengths and weaknesses. Though we do not aim to make a general claim about either being superior to the other across all specifications, we highlight several theoretical differences the analyst might consider and find that with some specifications, the two models behave very similarly, while each model out-predicts the other one the more the specific assumptions of the respective model are met.

Suggested Citation

  • Leifeld, Philip & Cranmer, Skyler J., 2019. "A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model," Network Science, Cambridge University Press, vol. 7(1), pages 20-51, March.
    • Handle: RePEc:cup:netsci:v:7:y:2019:i:01:p:20-51_00
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    Cited by:

    1. Xu, Yu & Hazée, Simon & So, Kevin Kam Fung & Li, K. Daisy & Malthouse, Edward Carl, 2021. "An evolutionary perspective on the dynamics of service platform ecosystems for the sharing economy," Journal of Business Research, Elsevier, vol. 135(C), pages 127-136.
    2. Linyan Wang & Haiqing Hu & Xianzhu Wang, 2022. "The Dynamic Evolution of the Structure of an Urban Housing Investment Niche Network and Its Underlying Mechanisms: A Case Study of 35 Large and Medium-Sized Cities in China," Sustainability, MDPI, vol. 14(6), pages 1-21, March.
    3. Duxbury, Scott W, 2019. "Mediation and Moderation in Statistical Network Models," SocArXiv 9bs4u, Center for Open Science.
    4. Ruck, Damian J. & Bentley, R. Alexander & Borycz, Joshua, 2021. "Early warning of vulnerable counties in a pandemic using socio-economic variables," Economics & Human Biology, Elsevier, vol. 41(C).
    5. Alexandra Goritz & Nina Kolleck & Helge Jörgens, 2019. "Education for Sustainable Development and Climate Change Education: The Potential of Social Network Analysis Based on Twitter Data," Sustainability, MDPI, vol. 11(19), pages 1-15, October.
    6. Zhou Nie, 2023. "Using Exponential Random Graph Models for Social Networks to Understand Meta-Communication in Digital Media," Social Sciences, MDPI, vol. 12(4), pages 1-11, April.
    7. Xiaoyi Shi & Xiaoxia Huang & Huifang Liu, 2022. "Research on the Structural Features and Influence Mechanism of the Low-Carbon Technology Cooperation Network Based on Temporal Exponential Random Graph Model," Sustainability, MDPI, vol. 14(19), pages 1-24, September.
    8. Liu, Linqing & Shen, Mengyun & Sun, Da & Yan, Xiaofei & Hu, Shi, 2022. "Preferential attachment, R&D expenditure and the evolution of international trade networks from the perspective of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    9. Zhao, Guimei & Li, Wenxiu & Geng, Yong & Bleischwitz, Raimund, 2023. "Uncovering the features of global antimony resource trade network," Resources Policy, Elsevier, vol. 85(PA).
    10. Anna Malinovskaya & Philipp Otto, 2021. "Online network monitoring," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1337-1364, December.

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