Robustness Study of Graph Neural FDE Models

The author investigates the robustness of graph neural fractional-order differential equation models, highlighting their superiority over traditional integer-order models in terms of output perturbation bounds. The approach integrates fractional calculus to enhance long-term memory and resilience against adversarial attacks.