A decoupled exponential random graph model for prediction of structure and attributes in temporal social networks

DOI10.1002/sam.10130OpenAlexW2085832817MaRDI QIDQ4969799

Zoran Obradovic, Vladimir Ouzienko, Yu-Hong Guo

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/sam.10130

zbMATH Keywords

predictorsocial network analysisexponential random graph modeldecoupled modeltemporal social networks

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Related Items (3)

A semiparametric Bayesian approach to epidemics, with application to the spread of the coronavirus MERS in South Korea in 2015 ⋮ Statistical inference for continuous‐time Markov processes with block structure based on discrete‐time network data ⋮ Exponential-family models of random graphs: inference in finite, super and infinite population scenarios

Uses Software

• Matlab
• Pajek
• TensorToolbox

Cites Work

• Logit models and logistic regressions for social networks. I: An introduction to Markov graphs and \(p^*\)
• Discrete temporal models of social networks
• Analysis of individual differences in multidimensional scaling via an \(n\)-way generalization of ``Eckart-Young decomposition
• A new status index derived from sociometric analysis
• Emergence of Scaling in Random Networks
• Efficient MATLAB Computations with Sparse and Factored Tensors
• Markov Graphs
• Monte Carlo sampling methods using Markov chains and their applications
• A new look at the statistical model identification
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