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Maximize the Probability of Union-Influenced in Social Networks

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Combinatorial Optimization and Applications (COCOA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13135))

Abstract

Nowadays, the social network plays an important role in advertisements and propaganda, and it creates the research of social influence. The prior works in social influence mainly consider the influence of individual or just the number of them. However, the union related is usual seen that is always together and each one is indispensable such as the team recruitment, in which a company or some business projections wish to recruit several candidates from different positions all to compose a team through the social networks. In this paper, different from targeted influence model, we consider such scenarios as an union-influence and propose the union-influence probability maximization problem (UIPM) to choose seeds to maximize the probability of the all nodes in an union are influenced. Unlike the most problems in previous social influence, the object function of UIPM is not submodularity or supermodularity. Then we design a data-driven \(\beta (1-\frac{1}{\epsilon })\)-approximation algorithm. At last we evaluate the performance on effectiveness and efficiency of the algorithms we proposed by the experiments in real-world social network datasets.

This work is supported by the National Natural Science Foundation of China (Grant NO. 12071478, 61972404), and partially by NSF 1907472.

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Notes

  1. 1.

    We said flipping an edge \(e_{uv}\) is that remove it with the probability \(1-p_{uv}\) from the graph and mark it to be “on” if the edge isn’t removed and otherwise be “off”.

  2. 2.

    http://networkrepository.com.

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Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, 100872, China

    Guoyao Rao, Yongcai Wang, Wenping Chen & Deying Li

  2. Department of Computer Science, University of Texas at Dallas, Richardson, TX, 75080, USA

    Weili Wu

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  1. Guoyao Rao
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  2. Yongcai Wang
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  3. Wenping Chen
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  4. Deying Li
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  5. Weili Wu
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Correspondence to Deying Li .

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Editors and Affiliations

  1. University of Texas at Dallas, Richardson, TX, USA

    Ding-Zhu Du

  2. University of New Brunswick, Fredericton, NB, Canada

    Donglei Du

  3. Tianjin University of Technology, Tianjin, China

    Chenchen Wu

  4. Beijing University of Technology, Beijing, China

    Dachuan Xu

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Rao, G., Wang, Y., Chen, W., Li, D., Wu, W. (2021). Maximize the Probability of Union-Influenced in Social Networks. In: Du, DZ., Du, D., Wu, C., Xu, D. (eds) Combinatorial Optimization and Applications. COCOA 2021. Lecture Notes in Computer Science(), vol 13135. Springer, Cham. https://doi.org/10.1007/978-3-030-92681-6_24

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  • DOI: https://doi.org/10.1007/978-3-030-92681-6_24

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  • Print ISBN: 978-3-030-92680-9

  • Online ISBN: 978-3-030-92681-6

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