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Cost-efficient viral marketing in online social networks

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Abstract

Word-of-mouth plays an important role in the spread of influence by means of a cascading manner, e.g., the promotion of new products, information dissemination, etc. Today’s Online Social Networks (OSNs) provide viral marketing for many companies to conduct business promotion, and the idea behind viral marketing comes from the word-of-mouth effects. Given a predefined budget (e.g., the set size k), influence maximization selects a set of initial users to help companies to promote their products. The budget-making often is of challenging owning to the hardness of trade-off between profit and cost. Most of recent research on influence maximization primarily study the problem of independent product promotion, but few take into account the impacts of other existing products that users have already owned. Moreover, the current solutions encounter scalability problem whenever facing a large scale social network. In this paper, we explore the viral marketing by defining a general influence maximization problem where a practical scenario of coexistence of multiple products is considered. To capture the impacts of other existing products, we put forward a new method to measure the influence between users. Different from previous work, the goal of our general problem is to maximize the profit/cost ratio which can avoid the difficulty of budget-making and reflects maximization effects better. Then, we propose a (\(\frac {1}{2} + \varepsilon \))-approximate algorithm with linear complexity to solve the problem, and further design a distributed implementation to achieve good scalability. Extensive experiments on real world datasets validate the effectiveness and efficiency of our solution.

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Notes

  1. https://www.statista.com/statistics/264810/number-of-monthly-active-facebook-users-worldwide/

  2. https://weibo.com/. Sina Weibo is one of the most popular microblog websites in China.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61502328, No. 61572337, No. 61672370, No. 61602333), the Open Project Program of the Provincial Key Laboratory for Computer Information Processing Technology (No. KJS1740), Jiangsu Planned Projects for Postdoctoral Research Funds (No. 1701173B), Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (No. 15KJB520032, No. 17KJB520036) and Collaborative Innovation Center of Novel Software Technology and Industrialization.

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, China

    Jingya Zhou, Jianxi Fan, Jin Wang, Xi Wang & Lingzhi Li

  2. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, China

    Jingya Zhou

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  1. Jingya Zhou
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  2. Jianxi Fan
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Correspondence to Jingya Zhou.

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Zhou, J., Fan, J., Wang, J. et al. Cost-efficient viral marketing in online social networks. World Wide Web 22, 2355–2378 (2019). https://doi.org/10.1007/s11280-018-0651-5

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