Research map / Learning theory and algorithms / Submodular and game algorithms
Selfish Creation of Social Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase edge-cost exponent from α=2 to α=3 (ablation) | SNCG best-move dynamics · SNCG simulation, n=3000 | average clustering coefficient: 0.415 -> 0.67 | Helped |
| Increase edge-cost exponent from α=2 to α=3 (ablation) | SNCG best-move dynamics · SNCG simulation, n=3000 | diameter: 8 -> 11 | Hurt |
| Increase edge-cost exponent from α=2 to α=3 (ablation) | SNCG best-move dynamics · SNCG simulation, n=3000 | average distance: 3.69 -> 5.17 | Hurt |
| Increase edge-cost exponent from α=2 to α=3 (ablation) | SNCG best-move dynamics · SNCG simulation, n=3000 | number of edges: 18059 -> 6019 | Hurt |
| Increase edge-cost exponent from α=2 to α=3 (ablation) | SNCG best-move dynamics · SNCG simulation, n=3000 | average degree: 12 -> 4.013 | Mixed |
| Use the SNCG best-move dynamics from sparse initial networks | SNCG · Simulations with n=3000 | power-law degree distribution | Helped |
| Use the SNCG best-move dynamics from sparse initial networks | SNCG · Simulations with n=1000 | average clustering coefficient | Helped |
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