45 citations · 83 across the 43 of their papers we have counts for
28 papers · 1 filter
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…
Optimal Sensor Placement via Graph-constrained Flow Matching
Feng Ji, Jingyang Dai, Wee Peng Tay +1
Optimal sensor placement is a fundamental problem in graph signal processing (GSP), where a limited number of sensors are deployed to reconstruct a continuous signal field. Existin…
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…
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…
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…
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…
Modeling Sparse Graph Sequences and Signals Using Generalized Graphons
Feng Ji, Xingchao Jian, Wee Peng Tay
Graphons are limit objects of sequences of graphs and are used to analyze the behavior of large graphs. Recently, graphon signal processing has been developed to study signal proce…
A sampling construction of graphon 1-norm convergence
Xingchao Jian, Feng Ji, Wee Peng Tay
In the short note, we describe a sampling construction that yields a sequence of graphons converging to a prescribed limit graphon in 1-norm. This convergence is stronger than the…
Generalized Graphon Process: Convergence of Graph Frequencies in Stretched Cut Distance
Xingchao Jian, Feng Ji, Wee Peng Tay
Graphons have traditionally served as limit objects for dense graph sequences, with the cut distance serving as the metric for convergence. However, sparse graph sequences converge…
Spectral Convergence of Complexon Shift Operators
Purui Zhang, Xingchao Jian, Feng Ji +2
Topological Signal Processing (TSP) utilizes simplicial complexes to model structures with higher order than vertices and edges. In this paper, we study the transferability of TSP…
The faces of Convolution: from the Fourier theory to algebraic signal processing
Feng Ji, Wee Peng Tay, Antonio Ortega
In this expository article, we provide a self-contained overview of the notion of convolution embedded in different theories: from the classical Fourier theory to the theory of alg…
Generalized signals on simplicial complexes
Feng Ji, Xingchao Jian, Wee Peng Tay +1
Topological signal processing (TSP) over simplicial complexes typically assumes observations associated with the simplicial complexes are real scalars. In this paper, we develop TS…
Distributional Signals for Node Classification in Graph Neural Networks
Feng Ji, See Hian Lee, Kai Zhao +2
In graph neural networks (GNNs), both node features and labels are examples of graph signals, a key notion in graph signal processing (GSP). While it is common in GSP to impose sig…
Graph signal processing with categorical perspective
Feng Ji, Xingchao Jian, Wee Peng Tay
In this paper, we propose a framework for graph signal processing using category theory. The aim is to generalize a few recent works on probabilistic approaches to graph signal pro…
On distributional graph signals
Feng Ji, Xingchao Jian, Wee Peng Tay
Graph signal processing (GSP) studies graph-structured data, where the central concept is the vector space of graph signals. To study a vector space, we have many useful tools up o…
Sampling of Correlated Bandlimited Continuous Signals by Joint Time-vertex Graph Fourier Transform
Zhongyi Ni, Feng Ji, Hang Sheng +2
When sampling multiple signals, the correlation between the signals can be exploited to reduce the overall number of samples. In this paper, we study the sampling theory of multipl…
On semi shift invariant graph filters
Feng Ji, See Hian Lee, Wee Peng Tay
In graph signal processing, one of the most important subjects is the study of filters, i.e., linear transformations that capture relations between graph signals. One of the most i…
Abstract message passing and distributed graph signal processing
Feng Ji, Yiqi Lu, Wee Peng Tay +1
Graph signal processing is a framework to handle graph structured data. The fundamental concept is graph shift operator, giving rise to the graph Fourier transform. While the graph…
To further understand graph signals
Feng Ji, Wee Peng Tay
Graph signal processing (GSP) is a framework to analyze and process graph-structured data. Many research works focus on developing tools such as Graph Fourier transforms (GFT), fil…
Recovery of Graph Signals from Sign Measurements
Wenwei Liu, Hui Feng, Kaixuan Wang +2
Sampling and interpolation have been extensively studied, in order to reconstruct or estimate the entire graph signal from the signal values on a subset of vertexes, of which most…
Graph Signal Processing over a Probability Space of Shift Operators
Feng Ji, Wee Peng Tay, Antonio Ortega
Graph signal processing (GSP) uses a shift operator to define a Fourier basis for the set of graph signals. The shift operator is often chosen to capture the graph topology. Howeve…
Signal processing with a distribution of graph operators
Feng Ji, Wee Peng Tay
In this paper, we develop a signal processing framework of a network without explicit knowledge of the network topology. Instead, we make use of knowledge on the distribution of op…
Sampling Theory of Bandlimited Continuous-Time Graph Signals
Feng Ji, Hui Feng, Hang Sheng +1
A continuous-time graph signal can be viewed as a time series of graph signals. It generalizes both the classical continuous-time signal and ordinary graph signal. Therefore, such…
Signal processing on simplicial complexes
Feng Ji, Giacomo Kahn, Wee Peng Tay
Theoretical development and applications of graph signal processing (GSP) have attracted much attention. In classical GSP, the underlying structures are restricted in terms of dime…