Graph trend filtering networks for recommendation
Recommender systems aim to provide personalized services to users and are playing an
increasingly important role in our daily lives. The key of recommender systems is to predict …
increasingly important role in our daily lives. The key of recommender systems is to predict …
Sampling signals on graphs: From theory to applications
The study of sampling signals on graphs, with the goal of building an analog of sampling for
standard signals in the time and spatial domains, has attracted considerable attention …
standard signals in the time and spatial domains, has attracted considerable attention …
Graph unrolling networks: Interpretable neural networks for graph signal denoising
We propose an interpretable graph neural network framework to denoise single or multiple
noisy graph signals. The proposed graph unrolling networks expand algorithm unrolling to …
noisy graph signals. The proposed graph unrolling networks expand algorithm unrolling to …
Verifying the smoothness of graph signals: A graph signal processing approach
L Dabush, T Routtenberg - IEEE Transactions on Signal …, 2024 - ieeexplore.ieee.org
Graph signal processing (GSP) deals with the representation, analysis, and processing of
structured data, ie graph signals that are defined on the vertex set of a generic graph. A …
structured data, ie graph signals that are defined on the vertex set of a generic graph. A …
Semi-supervised learning in network-structured data via total variation minimization
We provide an analysis and interpretation of total variation (TV) minimization for semi-
supervised learning from partially-labeled network-structured data. Our approach exploits an …
supervised learning from partially-labeled network-structured data. Our approach exploits an …
Localized linear regression in networked data
A Jung, N Tran - IEEE Signal Processing Letters, 2019 - ieeexplore.ieee.org
The network Lasso (nLasso) has been proposed recently as an efficient learning algorithm
for massive networked data sets (big data over networks). It extends the well-known least …
for massive networked data sets (big data over networks). It extends the well-known least …
Controllability of bandlimited graph processes over random time varying graphs
Controllability of complex networks arises in many technological problems involving social,
financial, road, communication, and smart grid networks. In many practical situations, the …
financial, road, communication, and smart grid networks. In many practical situations, the …
Graph signal sampling under stochastic priors
We propose a generalized sampling framework for stochastic graph signals. Stochastic
graph signals are characterized by graph wide sense stationarity (GWSS) which is an …
graph signals are characterized by graph wide sense stationarity (GWSS) which is an …
Graph signal denoising via unrolling networks
We propose an interpretable graph neural network framework to denoise single or multiple
noisy graph signals. The proposed graph unrolling networks expand algorithm unrolling to …
noisy graph signals. The proposed graph unrolling networks expand algorithm unrolling to …
Revisiting graph neural networks from hybrid regularized graph signal reconstruction
Graph neural networks (GNNs) have shown strong graph-structured data processing
capabilities. However, most of them are generated based on the message-passing …
capabilities. However, most of them are generated based on the message-passing …