A major line of work in graph signal processing [2] during the past 10 years has been to
design new transform methods that account for the underlying graph structure to identify and …

Recently, hypergraph learning has shown great potential in a variety of classification tasks.
However, existing hypergraph neural networks lack flexibility in modeling and extracting …

In a semisupervised learning scenario,(possibly noisy) partially observed labels are used as
input to train a classifier in order to assign labels to unclassified samples. In this paper, we …

We introduce a novel harmonic analysis for functions defined on the vertices of a strongly
connected directed graph (digraph) of which the random walk operator is the cornerstone …

Multiresolution analysis is important for understanding graph signals, which represent graph-
structured data. Wavelet filterbanks permit multiscale analysis and processing of graph …

Graph semi-supervised learning (GSSL) is a technique that uses a combination of labeled
and unlabeled nodes on a graph to determine a classifier for new, incoming data. This …