Research map / Probabilistic and scientific modeling / Variational and generative sampling
Completely random measures for modelling block-structured sparse networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Block-structured CRM model (CRMSBM) with degree correction via generalized gamma process, compared to CRM without blocks (K=1, eta=1) | CRMSBM · 11 network datasets (link prediction) | AUC on held-out edges | Helped |
| Adding degree heterogeneity (GGP sociability) on top of block structure, compared to pIRM (Poisson SBM without degree correction) | CRMSBM · 11 network datasets (link prediction), except Hagmann | AUC on held-out edges | Helped |
| Adding degree heterogeneity via GGP sociability compared to DCSBM | CRMSBM · 11 network datasets (link prediction) | AUC on held-out edges | No clear effect |
| Degree heterogeneity (sociability) modelling on Hagmann brain network | CRMSBM vs pIRM · Hagmann brain connectivity graph | AUC on held-out edges | No clear effect |
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