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Human Mobility Modeling during the COVID-19 Pandemic via Deep Graph Diffusion Infomax
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use DGDI instead of Inf-VAE | COVID-HK location prediction · COVID-HK | Recall@10: 3.73$\pm$0.2 -> 6.95$\pm$0.9 | Helped |
| use DGDI instead of Inf-VAE | COVID-HK location prediction · COVID-HK | MAP@10: 1.80$\pm$0.1 -> 3.25$\pm$0.2 | Helped |
| use DGDI instead of Inf-VAE | COVID-MLC location prediction · COVID-MLC | Recall@10: 8.64$\pm$0.3 -> 12.19$\pm$1.1 | Helped |
| use DGDI instead of Inf-VAE | COVID-MLC location prediction · COVID-MLC | MAP@10: 5.44$\pm$0.2 -> 6.37$\pm$0.3 | Helped |
| add a GNN to encode geometric graph information (ablation) | DGDI · COVID-HK | Recall@10: 2.28$\pm$0.3 -> 6.95$\pm$0.9 | Helped |
| add a GNN to encode geometric graph information (ablation) | DGDI · COVID-MLC | MAP@10: 4.58$\pm$0.1 -> 6.37$\pm$0.3 | Helped |
| add the graph-diffusion loss (λ1 greater than 0) (ablation) | DGDI · COVID-HK | MAP@10: 2.75$\pm$0.3 -> 3.25$\pm$0.2 | Helped |
| add the graph-diffusion loss (λ1 greater than 0) (ablation) | DGDI · COVID-MLC | Recall@10: 11.12$\pm$1.1 -> 12.19$\pm$1.1 | Helped |
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