Multidimensional Graph Fourier Transformation

Multidimensional Graph Fourier Transformation (GFT) leverages the spectral properties of graph-structured data to analyze and model complex systems, aiming to improve prediction accuracy and interpretability in various domains. Current research focuses on integrating GFT with neural networks, particularly graph neural networks (GNNs), to create powerful architectures like FourierGNNs and GFTNNs for tasks such as time series forecasting, network failure classification, and trajectory prediction. These methods offer advantages in handling high-dimensional data, capturing complex spatiotemporal dependencies, and generalizing to unseen data, impacting fields ranging from traffic management to network optimization.