Budget Allocation for Maximizing Viral Advertising in Social Networks

Viral advertising in social networks has arisen as one of the most promising ways to increase brand awareness and product sales. By distributing a limited budget, we can incentivize a set of users as initial adopters so that the advertising can start from the initial adopters and spread via sociM li...

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Bibliographic Details
Published inJournal of computer science and technology Vol. 31; no. 4; pp. 759 - 775
Main Authors Zhang, Bo-Lei, Qian, Zhu-Zhong, Li, Wen-Zhong, Tang, Bin, Lu, Sang-Lu, Fu, Xiaoming
Format Journal Article
LanguageEnglish
Published New York Springer US 01.07.2016
Springer Nature B.V
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China%Institute of Computer Science, University of G¨ottingen, G¨ottingen 37077, Germany
Subjects
Advertising
Algorithms
Allocations
Approximation
Artificial Intelligence
Budgeting
Budgets
Computer Science
Data Structures and Information Theory
Decisions
Greedy algorithms
Information Systems Applications (incl.Internet)
Initiatives
Maximization
Online advertising
Optimization
Product development
Regular Paper
Social networks
Software
Software Engineering
Statistical analysis
Studies
Theory of Computation
Viral marketing
分配
广告
最大化
病毒
目标用户
社交
社会网络
预算
United States > US
social network
influence maximization
information diffusion
submodular optimization
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Summary:Viral advertising in social networks has arisen as one of the most promising ways to increase brand awareness and product sales. By distributing a limited budget, we can incentivize a set of users as initial adopters so that the advertising can start from the initial adopters and spread via sociM links to become viral. Despite extensive researches in how to target the most influential users, a key issue is often neglected： how to incentivize the initial adopters. In the problem of influence maximization, the assumption is that each user has a fixed cost for being initial adopters, while in practice, user decisions for accepting the budget to be initial adopters are often probabilistic rather than deterministic. In this paper, we study optimal budget allocation in social networks to maximize the spread of viral advertising. In particular, a concave probability model is introduced to characterize each user＇s utility for being an initial adopter. Under this model, we show that it is NP-hard to find an optimal budget allocation for maximizing the spread of viral advertising. We then present a novel discrete greedy algorithm with near optimal performance, and further propose scaling-up techniques to improve the time-efficiency of our algorithm. Extensive experiments on real-world social graphs are implemented to validate the effectiveness of our algorithm in practice. The results show that our algorithm can outperform other intuitive heuristics significantly in almost all cases.
Bibliography:Viral advertising in social networks has arisen as one of the most promising ways to increase brand awareness and product sales. By distributing a limited budget, we can incentivize a set of users as initial adopters so that the advertising can start from the initial adopters and spread via sociM links to become viral. Despite extensive researches in how to target the most influential users, a key issue is often neglected： how to incentivize the initial adopters. In the problem of influence maximization, the assumption is that each user has a fixed cost for being initial adopters, while in practice, user decisions for accepting the budget to be initial adopters are often probabilistic rather than deterministic. In this paper, we study optimal budget allocation in social networks to maximize the spread of viral advertising. In particular, a concave probability model is introduced to characterize each user＇s utility for being an initial adopter. Under this model, we show that it is NP-hard to find an optimal budget allocation for maximizing the spread of viral advertising. We then present a novel discrete greedy algorithm with near optimal performance, and further propose scaling-up techniques to improve the time-efficiency of our algorithm. Extensive experiments on real-world social graphs are implemented to validate the effectiveness of our algorithm in practice. The results show that our algorithm can outperform other intuitive heuristics significantly in almost all cases.
11-2296/TP
social network, influence maximization, information diffusion, submodular optimization
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-016-1661-3