Abstract
The process of rumor spreading among people can be represented as information diffusion in social network. The scale of rumor spread changes greatly depending on starting vertices. If we can select vertices that contribute to large scale diffusion, the vertices are expected to be important for viral marketing. Given a network and the size of the starting vertices, the problem of selecting vertices for maximizing information diffusion is called as influence maximization problem. We propose three new approximation methods for influence maximization problem in dynamic networks. These methods are the extensions of previous methods for static networks to dynamic networks. Experiments for comparing the performance show that our proposed method achieves 1.5 times as much in the scale of diffusion and approximately 7.8 times faster in computational time at the maximum compared with previous heuristic methods.
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Acknowledgement
This work was supported by Tokyo Tech - Fuji Xerox Cooperative Research (Project Code KY260195), JSPS Grant-in-Aid for Scientific Research(B) (Grant Number 17H01785) and JST CREST (Grant Number JPMJCR1687).
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Murata, T., Koga, H. (2018). Methods for Influence Maximization in Dynamic Networks. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_77
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DOI: https://doi.org/10.1007/978-3-319-72150-7_77
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