45 citations · 83 across the 43 of their papers we have counts for
44 papers
Filter Learning for Subgraphs: Algebras and Performance Risk Bounds
Purui Zhang, Feng Ji, Yanan Zhao +2
Graph signal processing tasks that leverage spectral information typically assume access to the complete graph topology, which is often unavailable in practice. We propose a system…
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…
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…
Modulo Video Recovery via Selective Spatiotemporal Vision Transformer
Tianyu Geng, Feng Ji, Wee Peng Tay
Conventional image sensors have limited dynamic range, causing saturation in high-dynamic-range (HDR) scenes. Modulo cameras address this by folding incident irradiance into a boun…
Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networks
Yanan Zhao, Feng Ji, Jingyang Dai +4
Graph contrastive learning (GCL) learns node and graph representations by contrasting multiple views of the same graph. Existing methods typically rely on fixed, handcrafted views-…
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…
Distributed-Order Fractional Graph Operating Network
Kai Zhao, Xuhao Li, Qiyu Kang +5
We introduce the Distributed-order fRActional Graph Operating Network (DRAGON), a novel continuous Graph Neural Network (GNN) framework that incorporates distributed-order fraction…
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…
Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
Qiyu Kang, Kai Zhao, Qinxu Ding +5
We introduce the FRactional-Order graph Neural Dynamical network (FROND), a new continuous graph neural network (GNN) framework. Unlike traditional continuous GNNs that rely on int…
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…
Graph Neural Networks with a Distribution of Parametrized Graphs
See Hian Lee, Feng Ji, Kelin Xia +1
Traditionally, graph neural networks have been trained using a single observed graph. However, the observed graph represents only one possible realization. In many applications, th…
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…
Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
Feng Ji, See Hian Lee, Hanyang Meng +3
In node classification using graph neural networks (GNNs), a typical model generates logits for different class labels at each node. A softmax layer often outputs a label predictio…
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…
Node-Specific Space Selection via Localized Geometric Hyperbolicity in Graph Neural Networks
See Hian Lee, Feng Ji, Wee Peng Tay
Many graph neural networks have been developed to learn graph representations in either Euclidean or hyperbolic space, with all nodes' representations embedded in a single space. H…