Four permutation tests (ridge, group LASSO, and two CCA variants) detect association between node covariates and random-dot-product-graph latent structure, with consistency theorems and cheaper computation than prior dependency tests.
International Trade Network: Statistical Analysis and Modeling
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Globalization has rapidly advanced but exposed countries to supply chain disruptions, highlighted by the COVID-19 pandemic. This study exhaustively analyzes bilateral export data for 186 countries from 2018, 2020, and 2022, using Exponential Random Graph Models (ERGMs), to identify determinants of trade relationships, as well as Stochastic Block Models (SBMs), to characterize countries' roles in the trade network. Our findings show persistent, significant nodal characteristics driving bilateral trade and reveal no major structural changes in the trade network due to the pandemic.
fields
stat.ML 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Testing for correlation between network structure and high-dimensional node covariates
Four permutation tests (ridge, group LASSO, and two CCA variants) detect association between node covariates and random-dot-product-graph latent structure, with consistency theorems and cheaper computation than prior dependency tests.