13 citations · 14 across the 16 of their papers we have counts for
17 papers
Learning the Topology of a Simplicial Complex Using Noisy Simplicial Signals
Andrei Buciulea, Elvin Isufi, Geert Leus +1
Graphs are a fundamental tool for modeling the irregular (non-Euclidean) structure of complex data. However, they are inherently limited to representing pairwise relationships, mak…
Directed Graph Topology Inference via Graph Filter Identification
Rasoul Shafipour, Andrei Buciulea, Santiago Segarra +2
We address the problem of inferring a directed network from nodal measurements generated by linear diffusion dynamics on the sought graph. Observations are modeled as the outputs o…
Stationarity and Spectral Characterization of Random Signals on Simplicial Complexes
Madeline Navarro, Andrei Buciulea, Santiago Segarra +1
It is increasingly common for data to possess intricate structure, necessitating new models and analytical tools. Graphs, a prominent type of structure, can encode the relationship…
Estimating Fair Graphs from Graph-Stationary Data
Madeline Navarro, Andrei Buciulea, Samuel Rey +2
We estimate fair graphs from graph-stationary nodal observations such that connections are not biased with respect to sensitive attributes. Edges in real-world graphs often exhibit…
Learning Product Graphs from Two-dimensional Stationary Signals
Andrei Buciulea, Bishwadeep Das, Elvin Isufi +1
Graph learning aims to infer a network structure directly from observed data, enabling the analysis of complex dependencies in irregular domains. Traditional methods focus on scala…
Graph signal aware decomposition of dynamic networks via latent graphs
Bishwadeep Das, Andrei Buciulea, Antonio G. Marques +1
Dynamics on and of networks refer to changes in topology and node-associated signals, respectively and are pervasive in many socio-technological systems, including social, biologic…
Learning the Topology of a Simplicial Complex Using Simplicial Signals: A Greedy Approach
A. Buciulea, E. Isufi, G. Leus +1
Graphs are ubiquitous to model the irregular (non-Euclidean) structure of complex data, but they are limited to pairwise relationships and fail to model the complexities of the dat…
Online Network Inference from Graph-Stationary Signals with Hidden Nodes
Andrei Buciulea, Madeline Navarro, Samuel Rey +2
Graph learning is the fundamental task of estimating unknown graph connectivity from available data. Typical approaches assume that not only is all information available simultaneo…
Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior
Madeline Navarro, Samuel Rey, Andrei Buciulea +2
We propose estimating Gaussian graphical models (GGMs) that are fair with respect to sensitive nodal attributes. Many real-world models exhibit unfair discriminatory behavior due t…
Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals
Andrei Buciulea, Jiaxi Ying, Antonio G. Marques +1
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 Gaussia…
Mitigating Subpopulation Bias for Fair Network Topology Inference
Madeline Navarro, Samuel Rey, Andrei Buciulea +2
We consider fair network topology inference from nodal observations. Real-world networks often exhibit biased connections based on sensitive nodal attributes. Hence, different subp…
Learning graphs and simplicial complexes from data
Andrei Buciulea, Elvin Isufi, Geert Leus +1
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 th…
Joint Network Topology Inference in the Presence of Hidden Nodes
Madeline Navarro, Samuel Rey, Andrei Buciulea +2
We investigate the increasingly prominent task of jointly inferring multiple networks from nodal observations. While most joint inference methods assume that observations are avail…
Graph Learning from Gaussian and Stationary Graph Signals
Andrei Buciulea, Antonio G. Marques
Graphs have become pervasive tools to represent information and datasets with irregular support. However, in many cases, the underlying graph is either unavailable or naively obtai…
Joint graph learning from Gaussian observations in the presence of hidden nodes
Samuel Rey, Madeline Navarro, Andrei Buciulea +2
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 setups…
Joint inference of multiple graphs with hidden variables from stationary graph signals
Samuel Rey, Andrei Buciulea, Madeline Navarro +2
Learning graphs from sets of nodal observations represents a prominent problem formally known as graph topology inference. However, current approaches are limited by typically focu…
Learning Graphs from Smooth and Graph-Stationary Signals with Hidden Variables
Andrei Buciulea, Samuel Rey, Antonio G. Marques
Network-topology inference from (vertex) signal observations is a prominent problem across data-science and engineering disciplines. Most existing schemes assume that observations…