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A Linear Time Algorithm for Influence Maximization in Large-Scale Social Networks

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10638))

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Abstract

Influence maximization is the problem of finding k seed nodes in a given network as information sources so that the influence cascade can be maximized. To solve this problem both efficiently and effectively, in this paper we propose LAIM: a linear time algorithm for influence maximization in large-scale social networks. Our LAIM algorithm consists of two parts: (1) influence computation; and (2) seed nodes selection. The first part approximates the influence of any node using its local influence, which can be efficiently computed with an iterative algorithm. The second part selects seed nodes in a greedy manner based on the results of the first part. We theoretically prove that the time and space complexities of our algorithm are proportional to the network size. Experimental results on six real-world datasets show that our approach significantly outperforms other state-of-the-art algorithms in terms of influence spread, running time and memory usage.

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Notes

  1. 1.

    Our approach is also applicable to undirected networks.

  2. 2.

    http://snap.stanford.edu/data/.

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Acknowledgments

This work was supported in part by National Natural Science Foundation of China (No. 61702059), Graduate Student Research and Innovation Foundation of Chongqing City (No. CYS17024), Fundamental Research Funds for the Central Universities of China (No. 106112016CDJXY180003), China Postdoctoral Science Foundation (No. 2017M612913), Frontier and Application Foundation Research Program of Chongqing City (Nos. cstc2017jcyjAX0340, cstc2015jcyjA40006), Social Undertakings and Livelihood Security Science and Technology Innovation Funds of Chongqing City (No. cstc2017shmsA20013).

Author information

Authors and Affiliations

  1. College of Computer Science, Chongiqng University, Chongqing, China

    Hongchun Wu, Jiaxing Shang, Shangbo Zhou & Yong Feng

  2. Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing, China

    Hongchun Wu, Jiaxing Shang, Shangbo Zhou & Yong Feng

Authors
  1. Hongchun Wu
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  2. Jiaxing Shang
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  3. Shangbo Zhou
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  4. Yong Feng
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Corresponding author

Correspondence to Jiaxing Shang .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Wu, H., Shang, J., Zhou, S., Feng, Y. (2017). A Linear Time Algorithm for Influence Maximization in Large-Scale 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_76

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  • DOI: https://doi.org/10.1007/978-3-319-70139-4_76

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  • Online ISBN: 978-3-319-70139-4

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