The paper proves the first constant upper bounds on adaptivity gaps for influence maximization on in-arborescences [e/(e-1), 2e/(e-1)] and out-arborescences [e/(e-1), 2] under the IC model with full adoption feedback.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.SI 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
On Adaptivity Gaps of Influence Maximization under the Independent Cascade Model with Full Adoption Feedback
The paper proves the first constant upper bounds on adaptivity gaps for influence maximization on in-arborescences [e/(e-1), 2e/(e-1)] and out-arborescences [e/(e-1), 2] under the IC model with full adoption feedback.