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Better Bounds on the Adaptivity Gap of Influence Maximization under Full-adoption Feedback
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Relate adaptive policies to non-adaptive policies using hybrid policies and a sublinear-in-n analysis | Influence maximization under full-adoption feedback · General influence graphs | adaptivity gap upper bound: ⌈n^{1/3}⌉ | Helped |
| Use the refined hybrid-policy analysis for in-arborescences | Influence maximization under full-adoption feedback · In-arborescences | adaptivity gap upper bound: \frac{2e}{e-1}\approx 3.16 -> \frac{2e^2}{e^{2}-1}\approx 2.31 | Helped |
| Use the degree-excess parameter α to bound the reached-set boundary | Influence maximization under full-adoption feedback · α-bounded graphs | adaptivity gap upper bound: \min\left\{k,\frac{\alpha}{k}+2+ \frac{1}{1-(1-1/k)^k}\right\} | Helped |
| Exploit the path-or-cycle structure of 0-bounded graphs | Influence maximization under full-adoption feedback · 0-bounded graphs | adaptivity gap upper bound: \frac{3e^3}{e^{3}-1}\approx 3.16 | Helped |
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