Group-Level Influence Maximization with Budget Constraint

Influence maximization aims at finding a set of seed nodes in a social network that could influence the largest number of nodes. Existing work often focuses on the influence of individual nodes, ignoring that infecting different seeds may require different costs. Nonetheless, in many real-world appl...

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Bibliographic Details
Published inDatabase Systems for Advanced Applications Vol. 10177; pp. 625 - 641
Main Authors Yan, Qian, Huang, Hao, Gao, Yunjun, Lu, Wei, He, Qinming
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2017
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Budget Constraint
Cost Ratio
Directed Edge
Influence Maximization
Influence Propagation
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Summary:Influence maximization aims at finding a set of seed nodes in a social network that could influence the largest number of nodes. Existing work often focuses on the influence of individual nodes, ignoring that infecting different seeds may require different costs. Nonetheless, in many real-world applications such as advertising, advertisers care more about the influence of groups (e.g., crowds in the same areas or communities) rather than specific individuals, and are very concerned about how to maximize the influence with a limited budget. In this paper, we investigate the problem of group-level influence maximization with budget constraint. Towards this, we introduce a statistical method to reveal the influence relationship between the groups, based on which we propose a propagation model that can dynamically calculate the influence spread scope of seed groups, following by presenting a greedy algorithm called GLIMB to maximize the influence spread scope with a limited cost budget via the optimization of the seed-group portfolio. Theoretical analysis shows that GLIMB can guarantee an approximation ratio of at least $$(1-1/\sqrt{e})$$ . Experimental results on both synthetic and real-world data sets verify the effectiveness and efficiency of our approach.
Bibliography:Original Abstract: Influence maximization aims at finding a set of seed nodes in a social network that could influence the largest number of nodes. Existing work often focuses on the influence of individual nodes, ignoring that infecting different seeds may require different costs. Nonetheless, in many real-world applications such as advertising, advertisers care more about the influence of groups (e.g., crowds in the same areas or communities) rather than specific individuals, and are very concerned about how to maximize the influence with a limited budget. In this paper, we investigate the problem of group-level influence maximization with budget constraint. Towards this, we introduce a statistical method to reveal the influence relationship between the groups, based on which we propose a propagation model that can dynamically calculate the influence spread scope of seed groups, following by presenting a greedy algorithm called GLIMB to maximize the influence spread scope with a limited cost budget via the optimization of the seed-group portfolio. Theoretical analysis shows that GLIMB can guarantee an approximation ratio of at least \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(1-1/\sqrt{e})$$\end{document}. Experimental results on both synthetic and real-world data sets verify the effectiveness and efficiency of our approach.
ISBN:9783319557526
3319557521
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-55753-3_39