Improving Multi-objective Evolutionary Influence Maximization in Social Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10784)


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.


Influence maximization Social network Multi-objective evolutionary algorithms Seeding 



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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.EEMCSUniversity of TwenteEnschedeThe Netherlands
  2. 2.Integrated Signal Processing SystemsRWTH Aachen UniversityAachenGermany
  3. 3.DAUINPolitecnico di TorinoTorinoItaly
  4. 4.INRA, UMR 782 GMPAThiverval-GrignonFrance

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