A Boon or a Bane? An Examination of Social Communication in Social Trading

Deng, Jiaying; Yang, Mingwen; Pelster, Matthias; Tan, Yong

doi:10.2139/ssrn.3802038

Cited by 2 publications

(1 citation statement)

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“…The intuition behind the separable parameterization of STERGM is that the factors and processes that result in ties formation are different from those that result in ties dissolution. Many applications of STERGM on real-world data support the separable assumption for dynamic network analysis (e.g., Broekel and Bednarz, 2018;Uppala and Handcock, 2020;Xie et al, 2020;Deng et al, 2021). In contrast to the TERGM whose parameters simultaneously influence both incidence and duration, STERGM provides two sets of parameters, where one manages ties formation and the other manages ties dissolution.…”

Section: Background On Dynamic Binary Networkmentioning

confidence: 98%

A Partially Separable Temporal Model for Dynamic Valued Networks

Lun¹,

Chen²,

Padilla³

2022

Preprint

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The Exponential-family Random Graph Model (ERGM) is a powerful statistical model to represent the complicated structural dependencies of a binary network observed at a single time point. However, regarding dynamic valued networks whose observations are matrices of counts that evolve over time, the development of the ERGM framework is still in its infancy. We propose a Partially Separable Temporal ERGM (PST ERGM) for dynamic valued networks to facilitate the modeling of dyad value augmentation and dyad value diminution. Our parameter learning algorithms inherit state-of-the-art estimation techniques to approximate the maximum likelihood, by drawing Markov chain Monte Carlo (MCMC) samples conditioning on the network from the previous time step. We demonstrate the ability of the proposed model to interpret network dynamics and forecast temporal trends with real data.

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Section: Background On Dynamic Binary Networkmentioning

confidence: 98%