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Maximum likelihood-based influence maximization in social networks

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Abstract

Influence Maximization (IM) is an important issue in network analyzing which widely occurs in social networks. The IM problem aims to detect the top-k influential seed nodes that can maximize the influence spread. Although a lot of studies have been performed, a novel algorithm with a better balance between time-consumption and guaranteed performance is still needed. In this work, we present a novel algorithm called MLIM for the IM problem, which adopts maximum likelihood-based scheme under the Independent Cascade(IC) model. We construct thumbnails of the social network and calculate the L-value for each vertex using the maximum likelihood criterion. A greedy algorithm is proposed to sequentially choose the seeds with the smallest L-value. Empirical results on real-world networks have proved that the proposed method can provide a wider influence spreading while obtaining lower time consumption.

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Acknowledgments

This research was supported in part by the Chinese National Natural Science Foundation under Grant Nos. 61702441, 61772454, 61703362, 61602202, Natural Science Foundation of Jiangsu Province under contracts BK20170513, BK20160428, and the Blue Project of Yangzhou University.

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Authors and Affiliations

  1. College of Information Engineering of Yangzhou University, Yangzhou, 225127, China

    Wei Liu, Yun Li, Xin Chen & Jie He

  2. The Laboratory for Internet of Things and Mobile Internet Technology of Jiangsu Province, Huaiyin Institute of Technology, Huaiyin, 223002, China

    Wei Liu

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  1. Wei Liu
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  2. Yun Li
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  3. Xin Chen
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  4. Jie He
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Correspondence to Yun Li.

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Liu, W., Li, Y., Chen, X. et al. Maximum likelihood-based influence maximization in social networks. Appl Intell 50, 3487–3502 (2020). https://doi.org/10.1007/s10489-020-01747-8

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