Document Type
Article
Publication Date
2-27-2025
Abstract
In Coevolving Latent Space Networks with Attractors (CLSNA) models, nodes in a latent space represent social actors, and edges indicate their dynamic interactions. Attractors are added at the latent level to capture the notion of attractive and repulsive forces between nodes, borrowing from dynamical systems theory. However, CLSNA reliance on MCMC estimation makes scaling difficult, and the requirement for nodes to be present throughout the study period limit practical applications. We address these issues by (i) introducing a Stochastic gradient descent (SGD) parameter estimation method, (ii) developing a novel approach for uncertainty quantification using SGD, and (iii) extending the model to allow nodes to join and leave over time. Simulation results show that our extensions result in little loss of accuracy compared to MCMC, but can scale to much larger networks. We apply our approach to the longitudinal social networks of members of US Congress on the social media platform X. Accounting for node dynamics overcomes selection bias in the network and uncovers uniquely and increasingly repulsive forces within the Republican Party. Supplemental materials for the article are available online.
Recommended Citation
Pan, H., Zhu, X., Caliskan, C., Christenson, D. P., Spiliopoulos, K., Walker, D., & Kolaczyk, E. D. (2025). Stochastic Gradient Descent-based Inference for Dynamic Network Models with Attractors. Journal of Computational and Graphical Statistics, 1–10. https://doi.org/10.1080/10618600.2024.2447478
Supplemental material
Peer Reviewed
1
Copyright
Taylor & Francis
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Included in
American Politics Commons, Other Computer Sciences Commons, Social Influence and Political Communication Commons, Social Media Commons
Comments
This is an Accepted Manuscript version of an article accepted for publication in Journal of Computational and Graphical Statistics in 2025. https://doi.org/10.1080/10618600.2024.2447478 It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.