39 citations · 51 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…