Abstract
With the proliferation of online social networks (OSNs), the characterization of diffusion processes and influence maximization over such processes is a problem of relevance and importance. Although several algorithmic frameworks for identifying influential nodes exist in literature, there is a paucity of literature in the setting of competitive influence. In this paper, we present a novel mechanism design approach to study the initial seeding problem where the agents, represented by vertices in the social network, are economically rational. The principals compete for influence in the network by setting price and incentives to illicit high degree initial subscribers, which in turn profit by infecting their neighbors. We restrict attention to equilibrium strategies and comparative statics for the agents.
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Acknowledgments
We wish to thank Brendan Avent, Éva Czabarka, Stephen Fenner, and Alexander Matros for their helpful discussions and suggestions.
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Supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC) contract number D12PC000337, The US Government is authorized to reproduce and distribute reprints of this work for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/NBC, DTRA, or the US Government.
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Levet, M., Krishnan, S. (2017). A Mechanism Design Approach for Influence Maximization. In: Cheng, J., Hossain, E., Zhang, H., Saad, W., Chatterjee, M. (eds) Game Theory for Networks. GameNets 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 174. Springer, Cham. https://doi.org/10.1007/978-3-319-47509-7_6
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DOI: https://doi.org/10.1007/978-3-319-47509-7_6
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