Abstract
Online social networks play a major role in the spread of information on a very large scale. One of the major problems is to predict information propagation using social network interactions. The main purpose of this paper is to construct a heuristic model of a weighted graph based on empirical data that can outperform the existing models. We suggest a new approach of constructing the model of information based on matching specific weights to a given network.
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Acknowledgements
The article was supported within the framework of a subsidy by the Russian Academic Excellence Project ‘5-100’ and RFBR grant 16-29-09583 “Methodology, techniques and tools of recognition and counteraction to organised information campaigns on the Internet”. The dataset used in this research paper was provided by the NRU HSE International Laboratory for Applied Network Research.
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Laptsuev, R., Ananyeva, M., Meinster, D., Karpov, I., Makarov, I., Zhukov, L.E. (2018). Information Propagation Strategies in Online Social Networks. In: Kalyagin, V., Pardalos, P., Prokopyev, O., Utkina, I. (eds) Computational Aspects and Applications in Large-Scale Networks. NET 2016. Springer Proceedings in Mathematics & Statistics, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-96247-4_24
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