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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Sequential Estimation of Temporally Evolving Latent Space Network Models
AU - Turnbull, Kathryn
AU - Nemeth, Christopher
AU - Nunes, Matthew
AU - McCormick, Tyler
PY - 2023/3/31
Y1 - 2023/3/31
N2 - Dynamic network data describe interactions among a fixed population through time. This data type can be modelled using the latent space framework, where the probability of a connection forming is expressed as a function of low-dimensional latent coordinates associated with the nodes, and sequential estimation of model parameters can be achieved via Sequential Monte Carlo (SMC) methods. In this setting, SMC is a natural candidate for estimation which offers greater scalability than existing approaches commonly considered in the literature, allows for estimates to be conveniently updated given additional observations and facilitates both online and offline inference. A novel approach to sequentially infer parameters of dynamic latent space network models is proposed by building on techniques from the high-dimensional SMC literature. The scalability and performance of the proposed approach is explored via simulation, and the flexibility under model variants is demonstrated. Finally, a real-world dataset describing classroom contacts is analysed using the proposed methodology.
AB - Dynamic network data describe interactions among a fixed population through time. This data type can be modelled using the latent space framework, where the probability of a connection forming is expressed as a function of low-dimensional latent coordinates associated with the nodes, and sequential estimation of model parameters can be achieved via Sequential Monte Carlo (SMC) methods. In this setting, SMC is a natural candidate for estimation which offers greater scalability than existing approaches commonly considered in the literature, allows for estimates to be conveniently updated given additional observations and facilitates both online and offline inference. A novel approach to sequentially infer parameters of dynamic latent space network models is proposed by building on techniques from the high-dimensional SMC literature. The scalability and performance of the proposed approach is explored via simulation, and the flexibility under model variants is demonstrated. Finally, a real-world dataset describing classroom contacts is analysed using the proposed methodology.
KW - Statistical network analysis
KW - Sequential Monte Carlo
KW - Latent space
KW - Dynamic networks
U2 - 10.1016/j.csda.2022.107627
DO - 10.1016/j.csda.2022.107627
M3 - Journal article
VL - 179
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
SN - 0167-9473
M1 - 107627
ER -