Abstract
Although the competitive influence maximization (CIM) problem has been extensively studies, existing works ignore the fact that location information can play an important role in influence propagation. In this paper, we study the location-based competitive influence maximization (LCIM) problem, which aims to select an optimal set of users of a player or a company to maximize the influence for given query region, while at the same time their competitors are conducting a similar strategy. We propose a greedy algorithm with \((1-1/e-\epsilon )\) approximation ratio and a heuristic algorithm LCIM-MIA based on MIA structure. Experimental results on real-world datasets show that our methods often better than several baseline algorithms.
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Acknowledgements
This work is supported by VNU University of Engineering and Technology under project number CN 18.07.
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Vu, M.M., Hoang, H.X. (2019). Location-Based Competitive Influence Maximization in Social Networks. In: Tagarelli, A., Tong, H. (eds) Computational Data and Social Networks. CSoNet 2019. Lecture Notes in Computer Science(), vol 11917. Springer, Cham. https://doi.org/10.1007/978-3-030-34980-6_15
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DOI: https://doi.org/10.1007/978-3-030-34980-6_15
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