7 citations · 15 across the 18 of their papers we have counts for
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eess.SP2026
Graph Spectral Series and Its Signatures
Feng Ji, Jingyang Dai, Apostolos Evangelidis +3
Bandlimitedness is a central modeling assumption in graph signal processing (GSP), but many graph signals exhibit substantial energy throughout the graph-frequency domain. We intro…
eess.SP2026
Graph Distribution-valued Signals in Wasserstein Spaces: Theory and Applications
Yanan Zhao, Feng Ji, Xingchao Jian +1
We introduce a framework for graph signal processing (GSP) in which signals are represented as graph distribution-valued signals (GDSs), i.e., probability measures in a Wasserstein…
eess.SP2026
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…