1 citations · 1 across the 8 of their papers we have counts for
9 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…
Exact Bayesian Tracking of Dynamic Network Topologies
Victor M. Tenorio, Elvin Isufi, Geert Leus +1
Tracking the temporal evolution of network topologies is a fundamental challenge in social networks, epidemiology, and sensor systems, among others. This paper develops an exact Ba…
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…
Matched Topological Subspace Detector
Chengen Liu, Victor M. Tenorio, Antonio G. Marques +1
Topological spaces, represented by simplicial complexes, capture richer relationships than graphs by modeling interactions not only between nodes but also among higher-order entiti…
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…
Tracking Network Dynamics using Probabilistic State-Space Models
Victor M. Tenorio, Elvin Isufi, Geert Leus +1
This paper introduces a probabilistic approach for tracking the dynamics of unweighted and directed graphs using state-space models (SSMs). Unlike conventional topology inference m…
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…
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…