Influence maximization with partial feedback

The goal of Influence maximization (IM) is to select a set of most influential users in a social network subject to a budget constraint. In this work, we propose to study the adaptive IM problem under partial-feedback model. Our main contribution in this paper is to introduce a novel adaptive policy...

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Bibliographic Details
Published inOperations research letters Vol. 48; no. 1; pp. 24 - 28
Main Authors Tang, Shaojie, Yuan, Jing
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.01.2020
Subjects
Adaptive influence maximization
Partial feedback
Stochastic submodular maximization
Adaptive influence maximization
Stochastic submodular maximization
Partial feedback
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ISSN0167-6377
1872-7468
DOI10.1016/j.orl.2019.10.013

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Summary:The goal of Influence maximization (IM) is to select a set of most influential users in a social network subject to a budget constraint. In this work, we propose to study the adaptive IM problem under partial-feedback model. Our main contribution in this paper is to introduce a novel adaptive policy with bounded approximation ratio. One nice feature of our policy is that we can balance the delay and performance tradeoff by adjusting the value of a controlling parameter.
ISSN:0167-6377
1872-7468
DOI:10.1016/j.orl.2019.10.013