  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.
