A methodology using degree-corrected stochastic co-block models, spectral co-clustering, and singular vector smoothing tracks time-varying latent communities in directed networks from PVAR and VHAR models, with theoretical support and applications to employment and volatility data.
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Dynamic spectral co-clustering of directed networks to unveil latent community paths in VAR-type models
A methodology using degree-corrected stochastic co-block models, spectral co-clustering, and singular vector smoothing tracks time-varying latent communities in directed networks from PVAR and VHAR models, with theoretical support and applications to employment and volatility data.