Community-Based Acceptance Probability Maximization for Target Users on Social Networks

Social influence problems, such as Influence Maximization (IM), have been widely studied. But a key challenge remains: How does a company select a small size seed set such that the acceptance probability of target users is maximized? In this paper, we first propose the Acceptance Probability Maximiz...

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Bibliographic Details
Published inAlgorithmic Aspects in Information and Management Vol. 11343; pp. 293 - 305
Main Authors Yan, Ruidong, Zhu, Yuqing, Li, Deying, Wang, Yongcai
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2018
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Approximate algorithm
Community structure
Seed selection
Social influence
Submodularity
Online AccessGet full text
ISBN3030046176
9783030046170
ISSN0302-9743
1611-3349
DOI10.1007/978-3-030-04618-7_24

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Summary:Social influence problems, such as Influence Maximization (IM), have been widely studied. But a key challenge remains: How does a company select a small size seed set such that the acceptance probability of target users is maximized? In this paper, we first propose the Acceptance Probability Maximization (APM) problem, i.e., selecting a small size seed set S such that the acceptance probability of target users T is maximized. Then we use classical Independent Cascade (IC) model as basic information diffusion model. Based on this model, we prove that APM is NP-hard and the objective function is monotone non-decreasing as well as submodular. Considering community structure of social networks, we transform APM to Maximum Weight Hitting Set (MWHS) problem. Next, we develop a pipage rounding algorithm whose approximation ratio is ( $$1-1/e$$ ). Finally, we evaluate our algorithms by simulations on real-life social networks. Experimental results validate the performance of the proposed algorithm.
Bibliography:Original Abstract: Social influence problems, such as Influence Maximization (IM), have been widely studied. But a key challenge remains: How does a company select a small size seed set such that the acceptance probability of target users is maximized? In this paper, we first propose the Acceptance Probability Maximization (APM) problem, i.e., selecting a small size seed set S such that the acceptance probability of target users T is maximized. Then we use classical Independent Cascade (IC) model as basic information diffusion model. Based on this model, we prove that APM is NP-hard and the objective function is monotone non-decreasing as well as submodular. Considering community structure of social networks, we transform APM to Maximum Weight Hitting Set (MWHS) problem. Next, we develop a pipage rounding algorithm whose approximation ratio is (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1-1/e$$\end{document}). Finally, we evaluate our algorithms by simulations on real-life social networks. Experimental results validate the performance of the proposed algorithm.
ISBN:3030046176
9783030046170
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-04618-7_24