A wide range of data science problems can be modeled in terms of a graph (or network), eg,
social, sensor, communication, infrastructure, and biological networks. The nodes in a …

Motivation Elucidating the topology of gene regulatory networks (GRNs) from large single-
cell RNA sequencing datasets, while effectively capturing its inherent cell-cycle …

This paper proposes a blind detection problem for low pass graph signals. Without
assuming knowledge of the exact graph topology, we aim to detect if a set of graph signal …

Graph signal processing (GSP) has provided a wide range of powerful methodologies for
diverse learning tasks. While the data domain in GSP is fundamentally non-Euclidean, the …

The novel concept of signature graphs extends signed graphs by admitting multiple types of
partial similarity/agreement or dissimilarity/disagreement. Extending the concept of …

We introduce the novel concept of signature graphs. Contrary to conventional unsigned and
signed graphs, signature graphs capture partial similarity/agreement or dissimilarity …

Graph learning has been widely used in many fields to study the relationships between
different entities in a dataset. We present an optimization framework based on the proximal …

An important problem in graph signal processing (GSP) is to infer the topology of an
unknown graph from a set of observations on the nodes of the graph, ie graph signals …

Motivation Elucidating the topology of gene regulatory networks (GRNs) from large single-
cell RNA sequencing (scRNAseq) datasets, while effectively capturing its inherent cell-cycle …

Semi-supervised node classification is the task of assigning the nodes of a graph to different
classes based on the graph structure and the knowledge of a (small) portion of class …