- Title
- Evolutionary multiobjective optimization to target social network influentials in viral marketing
- Creator
- Robles, Juan Francisco; Chica, Manuel; Cordon, Oscar
- Relation
- Expert Systems with Applications Vol. 147, Issue 1 June 2020, no. 113183
- Publisher Link
- http://dx.doi.org/10.1016/j.eswa.2020.113183
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2020
- Description
- Marketers have an important asset if they effectively target social networks’ influentials. They can advertise products or services with free items or discounts to spread positive opinions to other consumers (i.e., word-of-mouth). However, main research on choosing the best influentials to target is single-objective and mainly focused on maximizing sales revenue. In this paper we propose a multiobjective approach to the influence maximization problem with the aim of increasing the revenue of viral marketing campaigns while reducing the costs. By using local social network metrics to locate influentials, we apply two evolutionary multiobjective optimization algorithms, NSGA-II and MOEA/D, a multiobjective adaptation of a single-objective genetic algorithm, and a greedy algorithm. Our proposal uses a realistic agent-based market framework to evaluate the fitness of the chromosomes by simulating the viral campaigns. The framework also generates, in a single run, a set of non-dominated solutions that allows marketers to consider multiple targeting options . The algorithms are evaluated on five network topologies and a real data-generated social network, showing that both MOEA/D and NSGA-II outperform the single-objective and the greedy approaches. More interestingly, we show a clear correlation between the algorithms’ performance and the diffusion features of the social networks.
- Subject
- viral marketing; influence maximization; influentials targeting; social networks; evolutionary multiobjective optimization; agent-based modeling
- Identifier
- http://hdl.handle.net/1959.13/1463024
- Identifier
- uon:46619
- Identifier
- ISSN:0957-4174
- Language
- eng
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