Research map / Graph learning / GNN architectures
Semiparametric Differential Graph Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| MCP nonconvex penalty instead of L1,1 penalty (LDGM-MCP vs LDGM-L1) | latent differential graph model · synthetic transelliptical, Frobenius error, all four settings | Frobenius norm estimation error: about 0.8 to 3.2 lower error | Helped |
| Latent differential graph model (LDGM-MCP) vs SepGlasso and DPM baselines | latent differential graph model · synthetic transelliptical, Frobenius and infinity norm errors | estimation error | Helped |
| Direct estimation of the difference matrix with MCP penalty vs separate graphical lasso estimation | differential graph estimation · real-world ovarian cancer TGF-beta and apoptosis pathways | qualitative identification of important genes | No clear effect |
| Using the MCP nonconvex penalty instead of SCAD | latent differential graph model · synthetic experiments | estimation and support recovery | No clear effect |
| DPM (L1,1-based direct difference estimator) at larger scale | DPM baseline · n=200, d=400 synthetic | runs completed | Hurt |
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