Tensor Graph

Tensor graphs represent data as multi-dimensional arrays (tensors) to capture complex relationships within and between data points, often improving upon traditional graph representations. Current research focuses on developing novel tensor graph convolutional networks and leveraging tensor decompositions (like t-SVD) for tasks such as multi-view clustering, dynamic graph representation learning, and graph classification. These advancements enable more efficient and accurate analysis of high-dimensional data, with applications ranging from machine learning model optimization to brain network analysis and improved performance prediction in large-scale computations.