This paper looks at the task of network topology inference, where the goal is to learn an
unknown graph from nodal observations. One of the novelties of the approach put forth is the …

We investigate the increasingly prominent task of jointly inferring multiple networks from
nodal observations. While most joint inference methods assume that observations are …

When facing graph signal processing tasks, it is typically assumed that the graph describing
the support of the signals is known. However, in many relevant applications the available …

This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph
structures from nodal signals. Our key contribution lies in modeling the signals as Gaussian …

Graph learning is the fundamental task of estimating unknown graph connectivity from
available data. Typical approaches assume that not only is all information available …

We consider fair network topology inference from nodal observations. Real-world networks
often exhibit biased connections based on sensitive nodal attributes. Hence, different …

Graph learning problems are typically approached by focusing on learning the topology of a
single graph when signals from all nodes are available. However, many contemporary …

Learning graphs from sets of nodal observations represents a prominent problem formally
known as graph topology inference. However, current approaches are limited by typically …

Graphs are widely used to represent complex information and signal domains with irregular
support. Typically, the underlying graph topology is unknown and must be estimated from …

This paper focuses on detecting the central nodes in a graph from partially observed graph
signals with unknown graph topology. We follow a general model which considers observed …