Chebyshev polynomial approximations are known for their optimal convergence rate in approximating functions. By incorporating these approximations into the construction of deep neural networks, ChebNet can achieve better performance and stability compared to other GNNs. This is particularly important when dealing with large-scale graph data, where computational efficiency and stability are crucial for practical applications.