4 papers · 1 filter
PolarFlow: Spectral Geometric Flow Matching for Directed Graph Generation via Polar Decomposition
Yanan Zhao, Feng Ji, Alba Carballo-Castro +4
Directed graphs model asymmetric relationships in biology, transportation, social networks, and electronic design automation. Recent discrete diffusion and flow-matching methods ge…
Uncertainty Principle for Vertex-Time Graph Signal Processing
Yanan Zhao, Xingchao Jian, Feng Ji +2
We present an uncertainty principle for graph signals in the vertex-time domain, unifying the classical time-frequency and graph uncertainty principles within a single framework. B…
Generalized Graph Signal Reconstruction via the Uncertainty Principle
Yanan Zhao, Xingchao Jian, Feng Ji +2
We introduce a novel uncertainty principle for generalized graph signals that extends classical time-frequency and graph uncertainty principles into a unified framework. By definin…
Frequency analysis and filter design for directed graphs with polar decomposition
Semin Kwak, Laura Shimabukuro, Antonio Ortega
In this study, we challenge the traditional approach of frequency analysis on directed graphs, which typically relies on a single measure of signal variation such as total variatio…