Research map / Graph learning / Graph algorithms and clustering
Network Regression with Graph Laplacians
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use global network regression when the network entries vary linearly with the predictor (ablation) | Network regression · Simulation scenario I | MISE: GF 0.467; GS 4.025; LF 7.116; LS 1.348 | Helped |
| Use local network regression when the predictor-network relationship is nonlinear (ablation) | Network regression · Simulation scenario III | MISE: GF 81.295; GS 87.481; LF 1.557; LS 2.227 | Helped |
| Use the square root metric instead of the Frobenius metric (ablation) | Local network regression · Simulation scenario I | MISE: LF 7.116; LS 1.348 | Helped |
| Use the square root metric instead of the Frobenius metric (ablation) | Global network regression · NYC yellow taxi networks | MSPE: 96.4% of that for d_F | Helped |
| Use global network regression with the square root metric instead of tensor-response regression | NYC yellow taxi networks · NYC yellow taxi networks | MSPE: 0.51 | Helped |
| Increase the sample size | Network regression · Simulation scenarios I–IV | MISE: n=50, 100, 200, 500, 1000 | Helped |
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