Graph Frequency

Graph frequency analysis examines the spectral properties of signals defined on graphs, aiming to understand and leverage the information encoded in different frequency components. Current research focuses on developing spectral graph neural networks and algorithms that effectively learn and utilize these frequencies, particularly exploring the relationship between graph frequency and network structure (e.g., homophily/heterophily). This work is significant for improving the performance of graph-based machine learning models across diverse applications, including 3D reconstruction and fMRI decoding, by enabling more efficient and accurate signal processing on complex graph data. Furthermore, research is extending graph frequency analysis to sparse graphs and signed graphs, broadening its applicability to a wider range of real-world problems.