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Opinion-Aware Influence Maximization: How To Maximize A Favorite Opinion In A Social Network?

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  • MEHRDAD AGHA MOHAMMAD ALI KERMANI

    (Department of Process Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran)

  • REZA GHESMATI

    (Amirkabir University of Technology, Tehran 15875-4413, Iran)

  • MASOUD JALAYER

    (Politecnico Di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy)

Abstract

Influence maximization is a well-known problem in the social network analysis literature which is to find a small subset of seed nodes to maximize the diffusion or spread of information. The main application of this problem in the real-world is in viral marketing. However, the classic influence maximization is disabled to model the real-world viral marketing problem, since the effect of the marketing message content and nodes’ opinions have not been considered. In this paper, a modified version of influence maximization which is named as “opinion-aware influence maximization” (OAIM) problem is proposed to make the model more realistic. In this problem, the main objective is to maximize the spread of a desired opinion, by optimizing the message content, rather than the number of infected nodes, which leads to selection of the best set of seed nodes. A nonlinear bi-objective mathematical programming model is developed to model the considered problem. Some transformation techniques are applied to convert the proposed model to a linear single-objective mathematical programming model. The exact solution of the model in small datasets can be obtained by CPLEX algorithm. For the medium and large-scale datasets, a new genetic algorithm is proposed to cope with the size of the problem. Experimental results on some of the well-known datasets show the efficiency and applicability of the proposed OAIM model. In addition, the proposed genetic algorithm overcomes state-of-the-art algorithms.

Suggested Citation

  • Mehrdad Agha Mohammad Ali Kermani & Reza Ghesmati & Masoud Jalayer, 2018. "Opinion-Aware Influence Maximization: How To Maximize A Favorite Opinion In A Social Network?," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-27, September.
    • Handle: RePEc:wsi:acsxxx:v:21:y:2018:i:06n07:n:s0219525918500224
    DOI: 10.1142/S0219525918500224
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    References listed on IDEAS

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    Cited by:

    1. Jan Lorenz & Martin Neumann, 2018. "Opinion Dynamics And Collective Decisions," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-9, September.

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