Abstract
In the context of social networks, maximizing influence means contacting the largest possible number of nodes starting from a set of seed nodes, and assuming a model for influence propagation. The real-world applications of influence maximization are of uttermost importance, and range from social studies to marketing campaigns. Building on a previous work on multi-objective evolutionary influence maximization, we propose improvements that not only speed up the optimization process considerably, but also deliver higher-quality results. State-of-the-art heuristics are run for different sizes of the seed sets, and the results are then used to initialize the population of a multi-objective evolutionary algorithm. The proposed approach is tested on three publicly available real-world networks, where we show that the evolutionary algorithm is able to improve upon the solutions found by the heuristics, while also converging faster than an evolutionary algorithm started from scratch.
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This article is based upon work from COST Action CA15140 ‘Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO)’ supported by the COST Agency.
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Bucur, D., Iacca, G., Marcelli, A., Squillero, G., Tonda, A. (2018). Improving Multi-objective Evolutionary Influence Maximization in Social Networks. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_9
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DOI: https://doi.org/10.1007/978-3-319-77538-8_9
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