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Guided Genetic Algorithm for the Influence Maximization Problem

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Book cover Computing and Combinatorics (COCOON 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10392))

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Abstract

Influence maximization is a hard combinatorial optimization problem. It requires the identification of an optimum set of k network vertices that triggers the activation of a maximum total number of remaining network nodes with respect to a chosen propagation model. The problem is appealing because it is provably hard and has a number of practical applications in domains such as data mining and social network analysis. Although there are many exact and heuristic algorithms for influence maximization, it has been tackled by metaheuristic and evolutionary methods as well. This paper presents and evaluates a new evolutionary method for influence maximization that employs a recent genetic algorithm for fixed–length subset selection. The algorithm is extended by the concept of guiding that prevents selection of infeasible vertices, reduces the search space, and effectively improves the evolutionary procedure.

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Acknowledgement

This work was supported by the Czech Science Foundation under the grant no. GA15-06700S, and by the projects SP2017/100 and SP2017/85 of the Student Grant System, VŠB-Technical University of Ostrava.

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Authors and Affiliations

  1. Department of Computer Science, VŠB Technical University of Ostrava, Ostrava, Czech Republic

    Pavel Krömer & Jana Nowaková

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  1. Pavel Krömer
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  2. Jana Nowaková
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Correspondence to Pavel Krömer .

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Editors and Affiliations

  1. Department of Computing, Hong Kong Polytechnic University, Hong Kong, China

    Yixin Cao

  2. Texas A&M University, College Station, Texas, USA

    Jianer Chen

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Krömer, P., Nowaková, J. (2017). Guided Genetic Algorithm for the Influence Maximization Problem. In: Cao, Y., Chen, J. (eds) Computing and Combinatorics. COCOON 2017. Lecture Notes in Computer Science(), vol 10392. Springer, Cham. https://doi.org/10.1007/978-3-319-62389-4_52

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  • DOI: https://doi.org/10.1007/978-3-319-62389-4_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62388-7

  • Online ISBN: 978-3-319-62389-4

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