OPINION-AWARE INFLUENCE MAXIMIZATION: HOW TO MAXIMIZE A FAVORITE OPINION IN A SOCIAL NETWORK?

Mehrdad Agha Mohammad Ali Kermani, Reza Ghesmati and Masoud Jalayer
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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

Advances in Complex Systems (ACS), 2018, vol. 21, issue 06n07, 1-27

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.

Keywords: Opinion-aware influence maximization; seed nodes; information diffusion; genetic algorithm; mathematical programming; opinion dynamics (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1142/S0219525918500224

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