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Review Sep 2026

Learning with Covariance Matrices: Principal Component Analysis Meets Learning with Graphs

This feature article provides an overview of the theoretical foundations for coVariance neural networks (VNNs), i.e., graph neural networks (GNNs) operating on covariance matrices as graphs. Covariance matrices are ubiquitous across domains, and hence, the deployment of GNNs often leverages graphs of pairwise statistic...

Saurabh Sihag, Andrea Cavallo, E. Isufi et al. · 0 citations
Preprint Sep 2026

Exact Bayesian Tracking of Dynamic Network Topologies

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 Bayesian tracker for unweighted, directed graphs using nodal signal observations. This framework yields the full posterior probability distrib...

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

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