Stochastic gradient descent methods and uncertainty quantification in extended CLSNA models

Oregon Convention Center 

Coevolving Latent Space Networks with Attractors (CLSNA), introduced by Zhu et al. (2023; JRSS-A), model dynamic networks where nodes in a latent space represent social actors, and edges indicate their interactions. Attractors are added at the latent level to capture the notion of attractive and repulsive forces between nodes, borrowing ideas from dynamical systems theory. The reliance of previous work on MCMC and the requirement for nodes to be present throughout the study period make scaling difficult. We address these issues by (i) introducing an SGD-based 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. Simulation results suggest that our approach results in little loss of accuracy compared to MCMC, but can scale to much larger networks. We revisit Zhu et al.'s analysis of longitudinal social networks for the US Congress from social media X and reinvestigate positive and negative forces among political elites. We now overcome an important selection bias in the previous study and reveal a negative force at play within the Republican Party.

Longitudinal social networks

Attractors

Partisan polarization

Dynamic networks analysis

Co-evolving network model 

Social Statistics Section