Abstract
Influence Maximization (\(\mathsf {IM}\)) is one of the key problems in viral marketing which has been paid much attention recently. Basically, \(\mathsf {IM}\) focuses on finding a set of k seed users on a social network to maximize the expected number of influenced nodes. However, most of related works consider only one player without competitors. In this paper, we investigate the Budgeted Competitive Influence Maximization (\({\mathsf {BCIM}}\)) problem within limited budget and time constraints which seeks a seed set nodes of a player or a company to propagate their products’s information while at the same time their competitors are conducting a similar strategy. We first analyze the complexity of this problem and show that the objective function is neither submodular nor suppermodular. We then apply Sandwich framework to design \({\mathsf {SPBA}}\), a randomized algorithm that guarantees a data dependent approximation factor.
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This work is partially supported by NSF EFRI 1441231 grant.
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Pham, C.V., Duong, H.V., Bui, B.Q., Thai, M.T. (2018). Budgeted Competitive Influence Maximization on Online Social Networks. In: Chen, X., Sen, A., Li, W., Thai, M. (eds) Computational Data and Social Networks. CSoNet 2018. Lecture Notes in Computer Science(), vol 11280. Springer, Cham. https://doi.org/10.1007/978-3-030-04648-4_2
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DOI: https://doi.org/10.1007/978-3-030-04648-4_2
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