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OPINION-AWARE INFLUENCE MAXIMIZATION: HOW TO MAXIMIZE A FAVORITE OPINION IN A SOCIAL NETWORK?
- Source :
-
Advances in Complex Systems . Sep-Nov2018, Vol. 21 Issue 6/7, pN.PAG-N.PAG. 27p. - Publication Year :
- 2018
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SOCIAL networks
*VIRAL marketing
*RUMOR
*INFLUENCE
*GENETIC algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 02195259
- Volume :
- 21
- Issue :
- 6/7
- Database :
- Academic Search Index
- Journal :
- Advances in Complex Systems
- Publication Type :
- Academic Journal
- Accession number :
- 134020821
- Full Text :
- https://doi.org/10.1142/S0219525918500224