7 citations · 15 across the 18 of their papers we have counts for
19 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…
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…
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…
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…
Simple Graph Contrastive Learning via Fractional-order Neural Diffusion Networks
Yanan Zhao, Feng Ji, Kai Zhao +6
Graph Contrastive Learning (GCL) has recently made progress as an unsupervised graph representation learning paradigm. GCL approaches can be categorized into augmentation-based and…
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…
PosDiffNet: Positional Neural Diffusion for Point Cloud Registration in a Large Field of View with Perturbations
Rui She, Sijie Wang, Qiyu Kang +5
Point cloud registration is a crucial technique in 3D computer vision with a wide range of applications. However, this task can be challenging, particularly in large fields of view…
DistilVPR: Cross-Modal Knowledge Distillation for Visual Place Recognition
Sijie Wang, Rui She, Qiyu Kang +4
The utilization of multi-modal sensor data in visual place recognition (VPR) has demonstrated enhanced performance compared to single-modal counterparts. Nonetheless, integrating a…
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…
Kernel Based Reconstruction for Generalized Graph Signal Processing
Xingchao Jian, Wee Peng Tay, Yonina C. Eldar
In generalized graph signal processing (GGSP), the signal associated with each vertex in a graph is an element from a Hilbert space. In this paper, we study GGSP signal reconstruct…
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…
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…
Wide-Sense Stationarity in Generalized Graph Signal Processing
Xingchao Jian, Wee Peng Tay
We consider statistical graph signal processing (GSP) in a generalized framework where each vertex of a graph is associated with an element from a Hilbert space. This general model…