257 citations · 1k across the 75 of their papers we have counts for
13 papers · 1 filter
Learning Graph Topology with Functional Priors via Bilevel Optimization
Chenyue Zhang, Shangyuan Liu, Hoi-To Wai +1
Learning graph topology of complex networks is challenging due to limited data availability and imprecise data models. Different from prior works that focus on structural priors wi…
Network Games Induced Prior for Graph Topology Learning
Chenyue Zhang, Shangyuan Liu, Hoi-To Wai +1
Learning the graph topology of a complex network is challenging due to limited data availability and imprecise data models. A common remedy in existing works is to incorporate prio…
On Detecting Low-pass Graph Signals under Partial Observations
Hoang-Son Nguyen, Hoi-To Wai
The application of graph signal processing (GSP) on partially observed graph signals with missing nodes has gained attention recently. This is because processing data from large gr…
Blind Graph Matching Using Graph Signals
Hang Liu, Anna Scaglione, Hoi-To Wai
Classical graph matching aims to find a node correspondence between two unlabeled graphs of known topologies. This problem has a wide range of applications, from matching identitie…
Detecting Low Pass Graph Signals via Spectral Pattern: Sampling Complexity and Applications
Chenyue Zhang, Yiran He, Hoi-To Wai
This paper proposes a blind detection problem for low pass graph signals. Without assuming knowledge of the exact graph topology, we aim to detect if a set of graph signal observat…
Product Graph Learning from Multi-attribute Graph Signals with Inter-layer Coupling
Chenyue Zhang, Yiran He, Hoi-To Wai
This paper considers learning a product graph from multi-attribute graph signals. Our work is motivated by the widespread presence of multilayer networks that feature interactions…
On the Stability of Low Pass Graph Filter With a Large Number of Edge Rewires
Hoang-Son Nguyen, Yiran He, Hoi-To Wai
Recently, the stability of graph filters has been studied as one of the key theoretical properties driving the highly successful graph convolutional neural networks (GCNs). The sta…
Detecting Central Nodes from Low-rank Excited Graph Signals via Structured Factor Analysis
Yiran He, Hoi-To Wai
This paper treats a blind detection problem to identify the central nodes in a graph from filtered graph signals. Unlike prior works which impose strong restrictions on the data mo…
Identifying First-order Lowpass Graph Signals using Perron Frobenius Theorem
Yiran He, Hoi-To Wai
This paper is concerned with the blind identification of graph filters from graph signals. Our aim is to determine if the graph filter generating the graph signals is first-order l…
A User Guide to Low-Pass Graph Signal Processing and its Applications
Raksha Ramakrishna, Hoi-To Wai, Anna Scaglione
The notion of graph filters can be used to define generative models for graph data. In fact, the data obtained from many examples of network dynamics may be viewed as the output of…
Estimating Centrality Blindly from Low-pass Filtered Graph Signals
Yiran He, Hoi-To Wai
This paper considers blind methods for centrality estimation from graph signals. We model graph signals as the outcome of an unknown low-pass graph filter excited with influences g…
Hybrid Inexact BCD for Coupled Structured Matrix Factorization in Hyperspectral Super-Resolution
Ruiyuan Wu, Hoi-To Wai, Wing-Kin Ma
This paper develops a first-order optimization method for coupled structured matrix factorization (CoSMF) problems that arise in the context of hyperspectral super-resolution (HSR)…
Block-Randomized Stochastic Proximal Gradient for Low-Rank Tensor Factorization
Xiao Fu, Shahana Ibrahim, Hoi-To Wai +2
This work considers the problem of computing the canonical polyadic decomposition (CPD) of large tensors. Prior works mostly leverage data sparsity to handle this problem, which is…