6 papers
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…
Graph Distribution-valued Signals: A Wasserstein Space Perspective
Yanan Zhao, Feng Ji, Xingchao Jian +1
We introduce a novel framework for graph signal processing (GSP) that models signals as graph distribution-valued signals (GDSs), which are probability distributions in the Wassers…
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…
Conformal Prediction for Multi-Source Detection on a Network
Xingchao Jian, Purui Zhang, Lan Tian +5
Detecting the origin of information or infection spread in networks is a fundamental challenge with applications in misinformation tracking, epidemiology, and beyond. We study the…
A Generalized Graph Signal Processing Framework for Multiple Hypothesis Testing over Networks
Xingchao Jian, Martin Gölz, Feng Ji +2
We consider the multiple hypothesis testing (MHT) problem over the joint domain formed by a graph and a measure space. On each sample point of this joint domain, we assign a hypoth…
A Graph Signal Processing Perspective of Network Multiple Hypothesis Testing with False Discovery Rate Control
Xingchao Jian, Martin Gölz, Feng Ji +2
We consider a multiple hypothesis testing problem in a sensor network over the joint spatio-temporal domain. The sensor network is modeled as a graph, with each vertex representing…