Gegenbauer Graph Neural Networks for Reconstructing Dynamic Graph Signals

GegenGNN, a novel graph neural network architecture, effectively reconstructs time-varying graph signals by leveraging Gegenbauer polynomials to capture both spatial and temporal dependencies in the data.