Abstract
Influence maximization aims to identify k nodes from a network such that the expected number of activated nodes by these k nodes is maximized, which is an important problem in viral marketing and has been extensively studied by the industrial and academic communities. We observe that the influence strength between users is diverse on different topics in real-world applications and the topic information plays a significant role in the influence maximization problem. In this paper, we study the topic-aware influence maximization problem. We propose a greedy algorithm with 1 − 1/e approximate ratio. Extensive experiments on real datasets show that our method efficiently and effectively finds k nodes on the given topics and outperforms state-of-the-art algorithms.
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Chu, Y., Zhao, X., Liu, S., Feng, J., Yi, J., Liu, H. (2014). An Efficient Method for Topic-Aware Influence Maximization. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8709. Springer, Cham. https://doi.org/10.1007/978-3-319-11116-2_55
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DOI: https://doi.org/10.1007/978-3-319-11116-2_55
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