Abstract
Cost-aware Targeted Viral Marketing (CTVM), a generalization of Influence Maximization (IM), has received a lot of attentions recently due to its commercial values. Previous approximation algorithms for this problem required a large number of samples to ensure approximate guarantee. In this paper, we propose an efficient approximation algorithm which uses fewer samples but provides the same theoretical guarantees based on generating and using important samples in its operation. Experiments on real social networks show that our proposed method outperforms the state-of-the-art algorithm which provides the same approximation ratio in terms of the number of required samples and running time.
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This work is partially supported by NSF CNS-1443905, NSF EFRI 1441231, and NSF NSF CNS-1814614 grants.
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Pham, C.V., Duong, H.V., Thai, M.T. (2019). Importance Sample-Based Approximation Algorithm for Cost-Aware Targeted Viral Marketing. 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_14
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DOI: https://doi.org/10.1007/978-3-030-34980-6_14
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