Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 11 references
Abstract
Graph signal processing provides a powerful framework for analyzing data defined over irregular network structures. Estimation of effective models from a set of timevarying graph signals requires capturing both temporal dynamics and graph-dependent statistical structures. Existing approaches that model time-vertex signals as stochastic processes typically assume a globally stationary model, which often fails to represent local variations that naturally arise across both temporal and graph dimensions. In this work, we address the problem of learning parametric models for graph signals exhibiting locally stationary behavior over time and graph. We propose a locally stationary time-vertex signal model that extends stationarity to a locally adaptive setting and develop an algorithm to learn the model parameters. Experiments on synthetic and real datasets demonstrate improved estimation accuracy over existing time-vertex methods.
The framework gives a nonparametric baseline for dynamic network analysis with explicit convergence guarantees and establishes nonparametric convergence rates in both block-model and Holder-smooth regimes.
NLDNAR‐CP is proposed, a novel change point detection method within a node‐specific NAR framework that accommodates heterogeneous temporal dependencies across variables and efficiently detects multiple structural breaks while preserving scalability to high‐dimensional networks.
This paper analyzes continuous normalized flow models parameterized by GNNs and shows that permutation equivariance is preserved for both the resulting continuous-time ordinary differential equations and their discrete numerical approximations used as graph signal samplers.
A fuzzy network jump model for clustering time-varying observations indexed by the nodes of a weighted graph that accurately recovers the true membership probabilities and outperforms competing clustering methods is introduced.
The results show that extreme-value characterization in stationary time series is enhanced when combining standard methods with visibility-graph-based detection, whereas for nonstationary data, where conventional approaches are often ill-posed, visibility graphs provide an effective alternative.
The definition of directed graph wide-sense stationarity is revisited, and the surrogate signals preserve covariance under the stationary assumption to demonstrate the feasibility of the scheme to detect irregular node covariance and benchmark the method against conventional schemes using the symmetrized graph.
Chun Hei Michael Chan, Alexandre Cionca, D. Van de Ville· 0 citations
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