We consider the problem of learning a graph from a given set of smooth graph signals. Our
graph learning approach is formulated as a constrained quadratic program in the edge …

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 …

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 …

We consider the problem of semi-supervised clustering for multiple (more than two) classes.
The proposed clustering algorithm uses the (dis) similarity of given data to learn the …

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 …

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) …

Graphen bieten ein nützliches Modell für viele Probleme da sie es ermöglichen auf flexible
und effiziente Art die Datenstruktur zu abstrahieren. Um Gebrauch von den wachsenden …