Abstract
Information spreading is one of the most important classes of dynamical process in complex networks, as it has relevance in many applications in real-world spreading phenomena, such as the spreading of virus in epidemics, advertising through the diffusion of social opinion, and the cascade failure of power networks and financial systems. For the prevention and the control of epidemic, or for the advertisement in online marketing, it is important to search for the set of source nodes to serve as super carriers that can spread information most effectively over a given period of time. We first use a small Watts-Strogatz network to investigate the important features of the super carriers through exhaustive search. We then design a mutation-only genetic algorithm to search for these super carriers and compare the efficiency of genetic algorithm as well as the quality of the set of nodes in terms of a measure of influence in information spreading with exhaustive search. Finally, we extend this search method to a larger artificial network as well as a real network to provide a set of candidates super carriers.
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Acknowledgments
K. C. Wong acknowledges the support of UROP funding from HKUST.
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Wong, K.C., Szeto, K.Y. (2020). Multiple Sources Influence Maximization in Complex Networks with Genetic Algorithm. In: Herrera, F., Matsui , K., RodrÃguez-González, S. (eds) Distributed Computing and Artificial Intelligence, 16th International Conference. DCAI 2019. Advances in Intelligent Systems and Computing, vol 1003 . Springer, Cham. https://doi.org/10.1007/978-3-030-23887-2_26
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DOI: https://doi.org/10.1007/978-3-030-23887-2_26
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