Graph Fourier

Graph Fourier transforms (GFTs) extend the concept of Fourier analysis to signals defined on graph structures, enabling the analysis of data with complex relationships. Current research focuses on applying GFTs within various machine learning models, such as graph neural networks (GNNs) and message-passing neural networks (MPNNs), to improve tasks like signal classification, anomaly detection, and graph representation learning. This approach is proving valuable in diverse fields, including network monitoring, speech recognition, and 3D point cloud processing, by offering improved feature extraction and robustness to noise or adversarial attacks. The development of efficient and generalized GFTs, particularly for dynamic and directed graphs, remains a key area of investigation.