In this tutorial, we provide a didactic treatment of the emerging topic of signal processing on
higher-order networks. Drawing analogies from discrete and graph signal processing, we …

Signal processing over graphs has recently attracted significant attention for dealing with the
structured data. Normal graphs, however, only model pairwise relationships between nodes …

Along with increasingly popular virtual reality applications, the three-dimensional (3D) point
cloud has become a fundamental data structure to characterize 3D objects and …

Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups
(eg, heart failure group and diabetes group) and identifying the temporal evolution of …

PARAFAC2 has demonstrated success in modeling irregular tensors, where the tensor
dimensions vary across one of the modes. An example scenario is modeling treatments …

Binary data with one-class missing values are ubiquitous in real-world applications. They
can be represented by irregular tensors with varying sizes in one dimension, where value …

Given a sparse time-evolving tensor, how can we effectively factorize it to accurately
discover latent patterns? Tensor decomposition has been extensively utilized for analyzing …

Understanding the response of human mobility to disruptive weather events is beneficial for
the development of urban risk mitigation and emergency response policies, thus enhancing …

Since high-order relationships among multiple brain regions-of-interests (ROIs) are helpful
to explore the pathogenesis of neurological diseases more deeply, hypergraph-based brain …

Hypergraph spectral analysis has emerged as an effective tool processing complex data
structures in data analysis. The surface of a three-dimensional (3D) point cloud, and the …