Enhanced graph-learning schemes driven by similar distributions of motifs
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 …
unknown graph from nodal observations. One of the novelties of the approach put forth is the …
Joint network topology inference in the presence of hidden nodes
We investigate the increasingly prominent task of jointly inferring multiple networks from
nodal observations. While most joint inference methods assume that observations are …
nodal observations. While most joint inference methods assume that observations are …
Robust graph filter identification and graph denoising from signal observations
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 …
the support of the signals is known. However, in many relevant applications the available …
Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals
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 …
structures from nodal signals. Our key contribution lies in modeling the signals as Gaussian …
Mitigating Subpopulation Bias for Fair Network Topology Inference
We consider fair network topology inference from nodal observations. Real-world networks
often exhibit biased connections based on sensitive nodal attributes. Hence, different …
often exhibit biased connections based on sensitive nodal attributes. Hence, different …
Joint graph learning from Gaussian observations in the presence of hidden nodes
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 …
single graph when signals from all nodes are available. However, many contemporary …
Joint inference of multiple graphs with hidden variables from stationary graph signals
Learning graphs from sets of nodal observations represents a prominent problem formally
known as graph topology inference. However, current approaches are limited by typically …
known as graph topology inference. However, current approaches are limited by typically …
Learning graphs and simplicial complexes from data
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 …
support. Typically, the underlying graph topology is unknown and must be estimated from …
Central nodes detection from partially observed graph signals
Y He, HT Wai - … 2023-2023 IEEE International Conference on …, 2023 - ieeexplore.ieee.org
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 …
signals with unknown graph topology. We follow a general model which considers observed …
A bi-objective model for network restoration considering fairness and graph signal-based functions
This paper explores the integration of graph signal processing, fairness, and cost to develop
bi-objective models for restoring disrupted networks. Graph signal processing enables the …
bi-objective models for restoring disrupted networks. Graph signal processing enables the …