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Stochastic Gradient Descent-based Inference for Dynamic Network Models with Attractors

https://doi.org/10.1080/10618600.2024.2447478

Pan, Hancong; Zhu, Xiaojing; Caliskan, Cantay; Christenson, Dino P; Spiliopoulos, Konstantinos; Walker, Dylan; Kolaczyk, Eric D (February 2025, Journal of Computational and Graphical Statistics)

Free, publicly-accessible full text available February 27, 2026
Disentangling positive and negative partisanship in social media interactions using a coevolving latent space network with attractors model

https://doi.org/10.1093/jrsssa/qnad008

Zhu, Xiaojing; Caliskan, Cantay; Christenson, Dino P.; Spiliopoulos, Konstantinos; Walker, Dylan; Kolaczyk, Eric D. (February 2023, Journal of the Royal Statistical Society Series A: Statistics in Society)

Abstract We develop a broadly applicable class of coevolving latent space network with attractors (CLSNA) models, where nodes represent individual social actors assumed to lie in an unknown latent space, edges represent the presence of a specified interaction between actors, and attractors are added in the latent level to capture the notion of attractive and repulsive forces. We apply the CLSNA models to understand the dynamics of partisan polarization in US politics on social media, where we expect Republicans and Democrats to increasingly interact with their own party and disengage with the opposing party. Using longitudinal social networks from the social media platforms Twitter and Reddit, we quantify the relative contributions of positive (attractive) and negative (repulsive) forces among political elites and the public, respectively.
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Advanced Electrocatalysts with Single-Metal-Atom Active Sites

https://doi.org/10.1021/acs.chemrev.0c00594

Wang, Yuxuan; Su, Hongyang; He, Yanghua; Li, Ligui; Zhu, Shangqian; Shen, Hao; Xie, Pengfei; Fu, Xianbiao; Zhou, Guangye; Feng, Chen; et al (November 2020, Chemical Reviews)
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