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Identifying a Set of Key Members in Social Networks Using SDP-Based Stochastic Search and Integer Programming Algorithms

Author

Listed:
  • Wentao Wu

    (Department of Industrial and Systems Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, USA)

  • Wai Kin Victor Chan

    (Department of Industrial and Systems Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, USA)

  • Lei Chi

    (EmblemHealth, 55 Water Street, NY 10041, USA)

  • Zhiguo Gong

    (Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macao, P. R. China)

Abstract

This paper presents two semi-definite programming (SDP) based methods to solve the Key Player Problem (KPP). The KPP is to identify a set of k nodes (i.e., key players) from a social network of size n such that the number of nodes connected to these k nodes is maximized. The KPP has applications in social diffusion and products adoption as it helps maximizing information diffusion and impact. We first formulate the KPP as an integer program (IP) and then convert it into an SDP formulation, which can be solved efficiently and produce a set of high quality candidate solutions. We develop an IP-based algorithm and a stochastic search (greedy) algorithm to find the final solution for the KPP. We compare our algorithms with existing methods in small and large networks with different network structures, including random graph, scale-free network, and community-based scale-free network (CSN). Computational results show that our algorithms are more efficient in solving the KPP in all networks. In addition, we examine how the network structure influences the nodes coverage. It is found that CSNs allow the highest nodes coverage due to their community and scale-free structure.

Suggested Citation

  • Wentao Wu & Wai Kin Victor Chan & Lei Chi & Zhiguo Gong, 2017. "Identifying a Set of Key Members in Social Networks Using SDP-Based Stochastic Search and Integer Programming Algorithms," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(03), pages 1-22, June.
    • Handle: RePEc:wsi:apjorx:v:34:y:2017:i:03:n:s0217595917500026
    DOI: 10.1142/S0217595917500026
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    References listed on IDEAS

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    1. Hema Yoganarasimhan, 2012. "Impact of social network structure on content propagation: A study using YouTube data," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 111-150, March.
    2. Jie Xu & Edward Huang & Chun-Hung Chen & Loo Hay Lee, 2015. "Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 32(03), pages 1-34.
    3. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    4. Stephen P. Borgatti, 2006. "Identifying sets of key players in a social network," Computational and Mathematical Organization Theory, Springer, vol. 12(1), pages 21-34, April.
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