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 …

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 …

Signed graphs have recently been found to offer advantages over unsigned graphs in a
variety of tasks. However, the problem of learning graph topologies has only been …

Image labeling and graph partitioning aim to divide a set of pixels or vertices into a specific
number of meaningful groups. In this paper, we propose effective truncated regularization …

We consider graph cut minimization in signed graphs with three clusters. To this end, we use
the signed total variation, which is convex and has shown promising results in our previous …

We consider the problem of unsupervised clustering on signed graphs, ie, graphs with
positive and negative edge weights. Motivated by signed cut minimization, we propose an …

In this work we consider the problem of semi-supervised node classification and extend the
method of Xu et al.[1] to a multiclass setting.[1] proposed a linearly constrained variant of the …

We consider the clustering problem for the labeled stochastic block model (LSBM) with non-
uniform class priors. We introduce a novel relaxation of the maximum a posteriory (MAP) …

We consider the problem of multiclass clustering on dynamic graphs. At each time instant,
the proposed algorithm performs local updates of the clusters in regions of nodes whose …