Abstract
Influence maximization, first proposed by Kempe, is the problem of finding seed nodes that maximizes the number of affected nodes. However, not only influenced number, but also influence layer is a crucial element which may play an important role in viral marketing. In this paper, we design a new framework, layer-prioritized influence maximization (LPIM), to address the problem of influence maximization with an emphasis on influence layer. The proposed framework is mainly composed of three parts: (1) graph clustering. (2) key node selection. (3) seed node detecting. We also demonstrate the effective and efficient of our proposed framework by experiments on large collaboration networks and complexity analysis respectively.
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Zhang, Q., Wu, Y., Xie, J. (2017). Layer-Prioritized Influence Maximization in Social Networks. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_64
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DOI: https://doi.org/10.1007/978-3-319-70139-4_64
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