Influence maximization with partial feedback

Shaojie Tang; Jing Yuan

doi:10.1016/j.orl.2019.10.013

Influence maximization with partial feedback

Shaojie Tang
, Jing Yuan

Management Science and Systems

University of Texas at Dallas

Research output: Contribution to journal › Article › peer-review

40 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	24-28
Number of pages	5
Journal	Operations Research Letters
Volume	48
Issue number	1
DOIs	https://doi.org/10.1016/j.orl.2019.10.013
State	Published - Jan 2020

Keywords

Adaptive influence maximization
Partial feedback
Stochastic submodular maximization

Access to Document

10.1016/j.orl.2019.10.013

Cite this

@article{39945a4ed55f4d4aa26b5fd5c994435b,

title = "Influence maximization with partial feedback",

abstract = "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.",

keywords = "Adaptive influence maximization, Partial feedback, Stochastic submodular maximization",

author = "Shaojie Tang and Jing Yuan",

note = "Publisher Copyright: {\textcopyright} 2019 Elsevier B.V.",

year = "2020",

month = jan,

doi = "10.1016/j.orl.2019.10.013",

language = "English",

volume = "48",

pages = "24--28",

journal = "Operations Research Letters",

issn = "0167-6377",

publisher = "Elsevier B.V.",

number = "1",

}

TY - JOUR

T1 - Influence maximization with partial feedback

AU - Tang, Shaojie

AU - Yuan, Jing

PY - 2020/1

Y1 - 2020/1

N2 - 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.

AB - 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.

KW - Adaptive influence maximization

KW - Partial feedback

KW - Stochastic submodular maximization

UR - https://www.scopus.com/pages/publications/85074666691

U2 - 10.1016/j.orl.2019.10.013

DO - 10.1016/j.orl.2019.10.013

M3 - Article

AN - SCOPUS:85074666691

SN - 0167-6377

VL - 48

SP - 24

EP - 28

JO - Operations Research Letters

JF - Operations Research Letters

IS - 1

ER -

Influence maximization with partial feedback

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this