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An Issue in the Martingale Analysis of the Influence Maximization Algorithm IMM

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Computational Data and Social Networks (CSoNet 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11280))

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Abstract

This paper explains a subtle issue in the martingale analysis of the IMM algorithm, a state-of-the-art influence maximization algorithm. Two workarounds are proposed to fix the issue, both requiring minor changes on the algorithm and incurring a slight penalty on the running time of the algorithm.

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Notes

  1. 1.

    The original paper has a typo here. It says to reset \(\ell \) to \(\ell (1+\log 2/\log n)\), but this is not necessary. Only resetting \(\ell \) to \(\ell + \log 2/ \log n\) is enough.

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Acknowledgment

The author would like to thank Jian Li for helpful discussions and verification on the issue explained in the paper.

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Authors and Affiliations

  1. Microsoft Research, Beijing, China

    Wei Chen

Authors
  1. Wei Chen
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Corresponding author

Correspondence to Wei Chen .

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Editors and Affiliations

  1. Texas Southern University, Houston, TX, USA

    Xuemin Chen

  2. Ira A. Fulton School of Engineering, Tempe, AZ, USA

    Arunabha Sen

  3. Texas Southern University, Houston, TX, USA

    Wei Wayne Li

  4. University of Florida, Gainesville, AL, USA

    My T. Thai

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Chen, W. (2018). An Issue in the Martingale Analysis of the Influence Maximization Algorithm IMM. In: Chen, X., Sen, A., Li, W., Thai, M. (eds) Computational Data and Social Networks. CSoNet 2018. Lecture Notes in Computer Science(), vol 11280. Springer, Cham. https://doi.org/10.1007/978-3-030-04648-4_24

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  • DOI: https://doi.org/10.1007/978-3-030-04648-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04647-7

  • Online ISBN: 978-3-030-04648-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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