45 citations · 83 across the 43 of their papers we have counts for
12 papers · 1 filter
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…
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…