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Incremental Influence Maximization for Dynamic Social Networks

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Data Science (ICPCSEE 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 728))

Abstract

Influence maximization is one fundamental and important problem to identify a set of most influential individuals to develop effective viral marketing strategies in social network. Most existing studies mainly focus on designing efficient algorithms or heuristics to find Top-K influential individuals for static network. However, when the network is evolving over time, the static algorithms have to be re-executed which will incur tremendous execution time. In this paper, an incremental algorithm DIM is proposed which can efficiently identify the Top-K influential individuals in dynamic social network based on the previous information instead of calculating from scratch. DIM is designed for Linear Threshold Model and it consists of two phases: initial seeding and seeds updating. In order to further reduce the running time, two pruning strategies are designed for the seeds updating phase. We carried out extensive experiments on real dynamic social network and the experimental results demonstrate that our algorithms could achieve good performance in terms of influence spread and significantly outperform those traditional static algorithms with respect to running time.

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Acknowledgment

This work was supported in part by the National Science Foundation of China (61632010, 61100048, 61370222), the Natural Science Foundation of Heilongjiang Province (F2016034), the Education Department of Heilongjiang Province (12531498).

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Authors and Affiliations

  1. School of Computer Science and Technology, Heilongjiang University, Harbin, 150001, China

    Yake Wang, Jinghua Zhu & Qian Ming

  2. Key Laboratory of Database and Parallel Computing of Heilongjiang Province, Harbin, China

    Jinghua Zhu & Qian Ming

Authors
  1. Yake Wang
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  2. Jinghua Zhu
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  3. Qian Ming
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Corresponding author

Correspondence to Jinghua Zhu .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Beiji Zou

  2. Harbin Engineering University, Harbin, China

    Qilong Han

  3. Harbin University of Science and Technology, Harbin, China

    Guanglu Sun

  4. Northeast Forestry University, Harbin, China

    Weipeng Jing

  5. Huaihua University, Huaihua, Hunan, China

    Xiaoning Peng

  6. Sciences of Country Tripod Institute of Data Science, Harbin, China

    Zeguang Lu

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© 2017 Springer Nature Singapore Pte Ltd.

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Wang, Y., Zhu, J., Ming, Q. (2017). Incremental Influence Maximization for Dynamic Social Networks. In: Zou, B., Han, Q., Sun, G., Jing, W., Peng, X., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-10-6388-6_2

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  • DOI: https://doi.org/10.1007/978-981-10-6388-6_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6387-9

  • Online ISBN: 978-981-10-6388-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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