Author

G. Leus

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Preprint Sep 2026

Learning the Topology of a Simplicial Complex Using Noisy Simplicial Signals

Graphs are a fundamental tool for modeling the irregular (non-Euclidean) structure of complex data. However, they are inherently limited to representing pairwise relationships, making them inadequate for datasets exhibiting higher-order interactions. Simplicial complexes (SCs) have emerged as a promising framework for...

Andrei Buciulea, E. Isufi, G. Leus et al. · 0 citations

#machine learning Preprint Sep 2026

Topological Signal Processing With Unoriented Operators

An unoriented TSP (UTSP) framework that replaces oriented boundaries with unoriented incidence matrices is studied, and it is shown that unoriented incidence and Laplacian matrices between arbitrary simplicial levels admit graph-like spectral properties.

Andrea Cavallo, Varun Sarathchandran, G. Leus et al. · 0 citations

Preprint Sep 2026

Exact Bayesian Tracking of Dynamic Network Topologies

This paper develops an exact Bayesian tracker for unweighted, directed graphs using nodal signal observations that yields the full posterior probability distribution over network states at each time step, naturally enabling uncertainty quantification, prediction, and principled decision-making.

Victor M. Tenorio, E. Isufi, G. Leus et al. · 0 citations