Fig. 4
From: Integrating graph and reinforcement learning for vaccination strategies in complex networks
The infection scale on five synthetic networks with 20% of nodes removed by various models, where \(\beta\) = 0.1, \(\gamma\) = 0.01 and \(\rho\) = 0.1. Each network consists of 500 nodes. The proposed approach outperforms the baseline methods across all datasets. This is primarily because it more effectively reduces the network’s conductivity and leads to smaller peaks in infection scale compared to the baselines. For instance, in the ER random network, immunizing 20% of the nodes identified by the proposed method results in a decrease of the infection scale peak from 86.8% to 75.6%, compared to using the GDM method. These results represent the average of 100 independent runs.
