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Fair Multi-influence Maximization in Competitive Social Networks

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Wireless Algorithms, Systems, and Applications (WASA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10251))

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Abstract

We study multi-influence competing in social networks. First we propose Timeliness Independent Cascade (TIC) model - a natural multi-influence propagation model. Second we propose FairInf problem: given several companies and their budgets, how to choose their separated seeds such that the overall influence spread is maximized and each individual company’s influence spread is fairly distributed. Third, we prove that when seeds for other companies are fixed, a company’s influence spread is monotone and submodular, which means greedy algorithm has the ratio of \((1-1/e)\). We design a greedy algorithm MG which runs quickly. At last, we conduct extensive experiments on real world social networks of different scales, and evaluate that our algorithm achieves the design goal.

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Acknowledgement

This work is partly supported by National Natural Science Foundation of China under grant 11671400.

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Correspondence to Deying Li .

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Yu, Y., Jia, J., Li, D., Zhu, Y. (2017). Fair Multi-influence Maximization in Competitive Social Networks. In: Ma, L., Khreishah, A., Zhang, Y., Yan, M. (eds) Wireless Algorithms, Systems, and Applications. WASA 2017. Lecture Notes in Computer Science(), vol 10251. Springer, Cham. https://doi.org/10.1007/978-3-319-60033-8_23

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  • DOI: https://doi.org/10.1007/978-3-319-60033-8_23

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