Abstract
Understanding how information propagates in real-life complex networks yields a better understanding of dynamical processes such as misinformation or epidemic spreading. With the recent resurgence of graph neural networks as a powerful predictive methodology, many network properties can be studied in terms of their predictability and as such offer a novel view on the studied process, with the direct application of fast predictions that are complementary to resource-intensive simulations. We investigated whether graph neural networks can be used to predict the effect of an epidemic, should it start from a given individual (patient zero). We reformulate this problem as node regression and demonstrate the high utility of network-based machine learning for a better understanding of the spreading effects. By being able to predict the effect of a given individual being the patient zero, the proposed approach offers potentially orders of magnitude faster risk assessment and potentially aids the adopted epidemic spreading analysis techniques.
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Notes
- 1.
Where S-Susceptible, I-Infected, R-Recovered, E-Exposed and W-Weakened.
- 2.
That can be found at https://github.com/smeznar/Epidemic-spreading-CN2020.
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Acknowledgments
The work of the last author (BŠ) was funded by the national research agency (ARRS)’s grant for junior researchers. The work of other authors was supported by the Slovenian Research Agency (ARRS) core research program P2-0103 and P6-0411, and research projects J7-7303, L7-8269, and N2-0078 (financed under the ERC Complementary Scheme).
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Mežnar, S., Lavrač, N., Škrlj, B. (2021). Prediction of the Effects of Epidemic Spreading with Graph Neural Networks. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_35
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