Research map / Graph learning / Knowledge and temporal graphs
Radon – Rapid Discovery of Topological Relations
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use sparse space tiling with MBB filtering and estimated-total-hypervolume-based source swapping | Radon · CLC subsets vs. NUTS, all seven evaluated relations | average speedup over Silk: 65.62 times faster | Helped |
| Use sparse space tiling with MBB filtering and estimated-total-hypervolume-based source swapping | Radon · CLC_m deduplication, all relations | average speedup over Silk: 715.16 times faster | Helped |
| Use the parallel Radon implementation | Radon · CLC_m deduplication, all relations | average speedup over Silk: 834.69 times on average | Helped |
| Use Radon instead of Strabon | Topological relation discovery on CLC subsets vs. NUTS · intersects | runtime | Hurt |
| Use Radon instead of Strabon | Topological relation discovery on CLC subsets vs. NUTS · all relations | average speedup over Strabon: 11.99 times faster | Helped |
| Use the average heuristic to select granularity (ablation) | Radon · 44 CLC subset experiments for intersects | best-runtime wins: 24 times out of 44 experiments | Helped |
| Use the median heuristic to select granularity instead of the average heuristic (ablation) | Radon · 44 CLC subset experiments for intersects | best-runtime wins: 17 times; 4% slower than the best measure on average when not being the best | Mixed |
| Use the minimum heuristic to select granularity (ablation) | Radon · 44 CLC subset experiments for intersects | best-runtime wins: 2 times; 34% worse on average when not best | Hurt |
| Use the maximum heuristic to select granularity (ablation) | Radon · 44 CLC subset experiments for intersects | best-runtime wins: 1 time; 61% worse on average when not best | Hurt |
| Increase parallel Radon threads from 1 to 8 (ablation) | Radon · CLC_m deduplication, equals | runtime: 24.11 -> 3.79 | Helped |
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