Mitigation of adversarial attacks on voter model dynamics by network heterogeneity

Voter model dynamics in complex networks are vulnerable to adversarial attacks. In particular, the voting outcome can be inverted by adding extremely small perturbations that are strategically generated in social networks, even when one opinion is dominant over the other. However, the mitigation of adversarial attacks on the voter model dynamics in complex networks has not been thoroughly investigated. Thus, we examined network structures that could mitigate adversarial attacks using model networks and real-world networks, considering that the network structure affects the voter model dynamics. Numerical simulations demonstrated that the heterogeneity of node degrees in the networks (degree heterogeneity) significantly mitigates adversarial attacks. In particular, for complex networks with a power-law degree distribution P(k)similar to k-?