Predicting epidemic threshold in complex networks by graph neural network.

ABSTRACT

To achieve precision in predicting an epidemic threshold in complex networks, we have developed a novel threshold graph neural network (TGNN) that takes into account both the network topology and the spreading dynamical process, which together contribute to the epidemic threshold. The proposed TGNN could effectively and accurately predict the epidemic threshold in homogeneous networks, characterized by a small variance in the degree distribution, such as Erdos-Rényi random networks. Usability has also been validated when the range of the effective spreading rate is altered. Furthermore, extensive experiments in ER networks and scale-free networks validate the adaptability of the TGNN to different network topologies without the necessity for retaining. The adaptability of the TGNN is further validated in real-world networks.