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Influence Maximization in Social Networks

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Optimization in Large Scale Problems

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 152))

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Abstract

Influence maximization (IM) is the problem of identifying a small subset of influential users such that influence spread in a network can be maximized. This problem has received significant attention from the Internet research community in the recent times, driven by many potential applications such as viral marketing, election campaign, counter-terrorism efforts, rumor control, and sales promotions, etc. In this paper, we perform a comparative review of the existing IM algorithms. First, we present a comprehensive study on existing IM approaches with their comparative theoretical analysis. Then, we present a comparative analysis of existing IM methods with respect to performance metrics. Finally, we discuss the challenges and future directions of the research.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, India

    Shashank Sheshar Singh, Ajay Kumar, Shivansh Mishra, Kuldeep Singh & Bhaskar Biswas

Authors
  1. Shashank Sheshar Singh
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  2. Ajay Kumar
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  3. Shivansh Mishra
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  4. Kuldeep Singh
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  5. Bhaskar Biswas
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Corresponding authors

Correspondence to Shashank Sheshar Singh or Kuldeep Singh .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, Mississippi State University, Starkville, MS, USA

    Mahdi Fathi

  2. School of Science and Engineering, Tecnológico de Monterrey, Monterrey, Mexico

    Marzieh Khakifirooz

  3. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Singh, S.S., Kumar, A., Mishra, S., Singh, K., Biswas, B. (2019). Influence Maximization in Social Networks. In: Fathi, M., Khakifirooz, M., Pardalos, P.M. (eds) Optimization in Large Scale Problems. Springer Optimization and Its Applications, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-030-28565-4_22

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