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Rating Prediction via Graph Signal Processing
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-0-387-85820-3openalex-snapshot
Recommender Systems Handbook
inferredcites →https://doi.org/10.1007/978-0-387-85820-3_1openalex-snapshot
Introduction to Recommender Systems Handbook
inferredcites →https://doi.org/10.1007/978-3-319-25040-3_70openalex-snapshot
Nonlinear Operators on Graphs via Stacks
inferredcites →https://doi.org/10.1007/s10208-009-9045-5openalex-snapshot
Exact Matrix Completion via Convex Optimization
inferredcites →https://doi.org/10.1016/j.sigpro.2016.05.037openalex-snapshot
Local measurement and reconstruction for noisy bandlimited graph signals
inferredcites →https://doi.org/10.1038/s41562-017-0260-9openalex-snapshot
Functional alignment with anatomical networks is associated with cognitive flexibility
inferredcites →https://doi.org/10.1090/s0002-9947-08-04511-xopenalex-snapshot
Sampling in Paley-Wiener spaces on combinatorial graphs
inferredcites →https://doi.org/10.1109/eusipco.2015.7362375openalex-snapshot
Interpolation of graph signals using shift-invariant graph filters
inferredcites →https://doi.org/10.1109/eusipco.2015.7362633openalex-snapshot
Toward an uncertainty principle for weighted graphs
inferredcites →https://doi.org/10.1109/eusipco.2016.7760497openalex-snapshot
Graph Laplacian distributed particle filtering
inferredcites →https://doi.org/10.1109/globalsip.2015.7418197openalex-snapshot
Edge-enhancing filters with negative weights
inferredcites →https://doi.org/10.1109/icassp.2012.6288775openalex-snapshot
Approximating signals supported on graphs
inferredcites →https://doi.org/10.1109/icassp.2014.6853724openalex-snapshot
A graph-based joint bilateral approach for depth enhancement
inferredcites →https://doi.org/10.1109/icassp.2016.7472541openalex-snapshot
Diffusion filtering of graph signals and its use in recommendation systems
inferredcites →https://doi.org/10.1109/icassp.2016.7472890openalex-snapshot
Bottleneck capacity of random graphs for connectomics
inferredcites →https://doi.org/10.1109/icassp.2017.7952934openalex-snapshot
Simultaneous low-rank component and graph estimation for high-dimensional graph signals: Application to brain imaging
inferredcites →https://doi.org/10.1109/jstsp.2007.910971openalex-snapshot
An Interior-Point Method for Large-Scale -Regularized Least Squares
inferredcites →https://doi.org/10.1109/jstsp.2016.2600859openalex-snapshot
Graph Frequency Analysis of Brain Signals
inferredcites →https://doi.org/10.1109/jstsp.2017.2726979openalex-snapshot
On the Graph Fourier Transform for Directed Graphs
inferredcites →https://doi.org/10.1109/mc.2009.263openalex-snapshot
Matrix Factorization Techniques for Recommender Systems
inferredcites →https://doi.org/10.1109/msp.2012.2235192openalex-snapshot
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
inferredcites →https://doi.org/10.1109/msp.2014.2329213openalex-snapshot
Big Data Analysis with Signal Processing on Graphs: Representation and processing of massive data sets with irregular structure
inferredcites →https://doi.org/10.1109/spcom.2016.7746675openalex-snapshot
Graph Fourier transform based on directed Laplacian
inferredcites →https://doi.org/10.1109/tip.2009.2022459openalex-snapshot
Learning to Sense Sparse Signals: Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization
inferredcites →https://doi.org/10.1109/tip.2014.2362059openalex-snapshot
A General Framework for Regularized, Similarity-Based Image Restoration
inferredcites →https://doi.org/10.1109/tip.2016.2529506openalex-snapshot
Graph-Based Compression of Dynamic 3D Point Cloud Sequences
inferredcites →https://doi.org/10.1109/tip.2017.2651400openalex-snapshot
Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain
inferredcites →https://doi.org/10.1109/tsipn.2018.2819018openalex-snapshot
Robust Semisupervised Graph Classifier Learning With Negative Edge Weights
inferredcites →https://doi.org/10.1109/tsp.2013.2238935openalex-snapshot
Discrete Signal Processing on Graphs
inferredcites →https://doi.org/10.1109/tsp.2014.2321121openalex-snapshot
Discrete Signal Processing on Graphs: Frequency Analysis
inferredcites →https://doi.org/10.1109/tsp.2015.2469645openalex-snapshot
Discrete Signal Processing on Graphs: Sampling Theory
inferredcites →https://doi.org/10.1109/tsp.2015.2507546openalex-snapshot
Sampling of Graph Signals With Successive Local Aggregations
inferredcites →https://doi.org/10.1109/tsp.2016.2544747openalex-snapshot
Subgraph-Based Filterbanks for Graph Signals
inferredcites →https://doi.org/10.1109/tsp.2017.2703660openalex-snapshot
Optimal Graph-Filter Design and Applications to Distributed Linear Network Operators
inferredcites →https://doi.org/10.1109/tsp.2017.2775589openalex-snapshot
A Time-Vertex Signal Processing Framework: Scalable Processing and Meaningful Representations for Time-Series on Graphs
inferredcites →https://doi.org/10.1109/tsp.2018.2879624openalex-snapshot
CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters
inferredcites →https://doi.org/10.1137/080738970openalex-snapshot
A Singular Value Thresholding Algorithm for Matrix Completion
inferredcites →https://doi.org/10.1145/371920.372071openalex-snapshot
Item-based collaborative filtering recommendation algorithms
inferredcites →https://doi.org/10.23919/eusipco.2017.8081498openalex-snapshot
Collaborative filtering via graph signal processing
inferredcites →https://doi.org/10.48550/arxiv.1301.7363openalex-snapshot
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
inferredcites →https://doi.org/10.48550/arxiv.1704.06803openalex-snapshot
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s00521-024-10866-zopenalex-snapshot
In-depth survey: deep learning in recommender systems—exploring prediction and ranking models, datasets, feature analysis, and emerging trends
inferred← citeshttps://doi.org/10.1016/j.dsp.2021.103009openalex-snapshot
Generalized Newton methods for graph signal matrix completion
inferred← citeshttps://doi.org/10.1016/j.dsp.2022.103792openalex-snapshot
Robust blind separation of smooth graph signals using minimization of graph regularized mutual information
inferred← citeshttps://doi.org/10.1016/j.eswa.2022.117097openalex-snapshot
A recommendation prediction method based on the estimation of PSD of sampled signals on graph
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.119867openalex-snapshot
Rating prediction based on the graph Fourier basis and PSD estimation from the perspective of graph signal reconstruction
inferred← citeshttps://doi.org/10.1016/j.ipm.2020.102459openalex-snapshot
Accuracy-diversity trade-off in recommender systems via graph convolutions
inferred← citeshttps://doi.org/10.1016/j.sigpro.2019.107366openalex-snapshot
Deep latent factor model for collaborative filtering
inferred← citeshttps://doi.org/10.1016/j.sigpro.2020.107749openalex-snapshot
Observing and tracking bandlimited graph processes from sampled measurements
inferred← citeshttps://doi.org/10.1016/j.sigpro.2024.109535openalex-snapshot
Graph-based minimum error entropy Kalman filtering
inferred← citeshttps://doi.org/10.1109/access.2021.3053270openalex-snapshot
A Computer-Based Method to Determine Predictive Potential of Distance-Spectral Descriptors for Measuring the π-Electronic Energy of Benzenoid Hydrocarbons With Applications
inferred← citeshttps://doi.org/10.1109/access.2022.3149755openalex-snapshot
On the Fractionalization of the Shift Operator on Graphs
inferred← citeshttps://doi.org/10.1109/dslw53931.2022.9820066openalex-snapshot
Graph Filtering Over Expanding Graphs
inferred← citeshttps://doi.org/10.1109/icassp.2019.8682975openalex-snapshot
Aggregation Graph Neural Networks
inferred← citeshttps://doi.org/10.1109/icassp.2019.8683050openalex-snapshot
Attention-based Graph Convolutional Network for Recommendation System
inferred← citeshttps://doi.org/10.1109/icassp.2019.8683787openalex-snapshot
Matrix Completion with Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference
inferred← citeshttps://doi.org/10.1109/icassp39728.2021.9414275openalex-snapshot
Topological Volterra Filters
inferred← citeshttps://doi.org/10.1109/icassp40776.2020.9053882openalex-snapshot
The Graphon Fourier Transform
inferred← citeshttps://doi.org/10.1109/icassp40776.2020.9054341openalex-snapshot
Stability of Graph Neural Networks to Relative Perturbations
inferred← citeshttps://doi.org/10.1109/icassp43922.2022.9746912openalex-snapshot
Training Stable Graph Neural Networks Through Constrained Learning
inferred← citeshttps://doi.org/10.1109/icassp43922.2022.9747156openalex-snapshot
Learning Expanding Graphs for Signal Interpolation
inferred← citeshttps://doi.org/10.1109/icassp49357.2023.10094894openalex-snapshot
Training Graph Neural Networks on Growing Stochastic Graphs
inferred← citeshttps://doi.org/10.1109/icassp49660.2025.10887710openalex-snapshot
Bayesian Filtering on Graphs
inferred← citeshttps://doi.org/10.1109/ieeeconf44664.2019.9048767openalex-snapshot
Convolutional Graph Neural Networks
inferred← citeshttps://doi.org/10.1109/ieeeconf56349.2022.10051821openalex-snapshot
Learning Stable Graph Neural Networks via Spectral Regularization
inferred← citeshttps://doi.org/10.1109/ieeeconf56349.2022.10052045openalex-snapshot
Online Filtering over Expanding Graphs
inferred← citeshttps://doi.org/10.1109/is3c50286.2020.00060openalex-snapshot
A Temperature Data Denoising Method Using Laplacian Matrix and Neumann Series
inferred← citeshttps://doi.org/10.1109/itw61385.2024.10806975openalex-snapshot
Improved Stability Bounds for Graph Convolutional Neural Networks Under Graph Perturbations
inferred← citeshttps://doi.org/10.1109/jproc.2021.3055400openalex-snapshot
Graph Neural Networks: Architectures, Stability, and Transferability
inferred← citeshttps://doi.org/10.1109/lsp.2021.3123459openalex-snapshot
Accelerated Graph Learning From Smooth Signals
inferred← citeshttps://doi.org/10.1109/lsp.2023.3298280openalex-snapshot
Graph Learning From Signals With Smoothness Superimposed by Regressors
inferred← citeshttps://doi.org/10.1109/lsp.2026.3704060openalex-snapshot
Stochastic Sequential Decision Making Over Expanding Networks With Graph Filtering
inferred← citeshttps://doi.org/10.1109/msp.2018.2890143openalex-snapshot
Connecting the Dots: Identifying Network Structure via Graph Signal Processing
inferred← citeshttps://doi.org/10.1109/msp.2020.3016143openalex-snapshot
Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks
inferred← citeshttps://doi.org/10.1109/ojsp.2021.3056897openalex-snapshot
Node-Adaptive Regularization for Graph Signal Reconstruction
inferred← citeshttps://doi.org/10.1109/ojsp.2024.3444707openalex-snapshot
Multilinear Kernel Regression and Imputation via Manifold Learning
inferred← citeshttps://doi.org/10.1109/sampta45681.2019.9030974openalex-snapshot
Average sampling and average splines on combinatorial graphs
inferred← citeshttps://doi.org/10.1109/ssp49050.2021.9513824openalex-snapshot
Data-Driven Personalized Cervical Cancer Risk Prediction: A Graph-Perspective
inferred← citeshttps://doi.org/10.1109/tnnls.2022.3165554openalex-snapshot
A Spatial–Temporal Graph Model for Pronunciation Feature Prediction of Chinese Poetry
inferred← citeshttps://doi.org/10.1109/tpami.2023.3311912openalex-snapshot
Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis
inferred← citeshttps://doi.org/10.1109/tsipn.2022.3206578openalex-snapshot
Task-Aware Connectivity Learning for Incoming Nodes Over Growing Graphs
inferred← citeshttps://doi.org/10.1109/tsipn.2023.3341408openalex-snapshot
Stability of Aggregation Graph Neural Networks
inferred← citeshttps://doi.org/10.1109/tsipn.2025.3611264openalex-snapshot
Exploiting the Structure of Two Graphs With Graph Neural Networks
inferred← citeshttps://doi.org/10.1109/tsp.2018.2889430openalex-snapshot
2018 Index IEEE Transactions on Signal Processing Vol. 66
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Invariance-Preserving Localized Activation Functions for Graph Neural Networks
inferred← citeshttps://doi.org/10.1109/tsp.2020.2988784openalex-snapshot
Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift
inferred← citeshttps://doi.org/10.1109/tsp.2020.3026980openalex-snapshot
Stability Properties of Graph Neural Networks
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Exploiting Relevance for Online Decision-Making in High-Dimensions
inferred← citeshttps://doi.org/10.1109/tsp.2021.3139208openalex-snapshot
Quantization Analysis and Robust Design for Distributed Graph Filters
inferred← citeshttps://doi.org/10.1109/tsp.2022.3192606openalex-snapshot
Wide and Deep Graph Neural Network With Distributed Online Learning
inferred← citeshttps://doi.org/10.1109/tsp.2023.3242374openalex-snapshot
Learning by Transference: Training Graph Neural Networks on Growing Graphs
inferred← citeshttps://doi.org/10.1109/tsp.2023.3293716openalex-snapshot
Convolutional Filters and Neural Networks With Noncommutative Algebras
inferred← citeshttps://doi.org/10.1109/tsp.2023.3318471openalex-snapshot
Graphon Pooling for Reducing Dimensionality of Signals and Convolutional Operators on Graphs
inferred← citeshttps://doi.org/10.1109/tsp.2024.3349788openalex-snapshot
Graph Filters for Signal Processing and Machine Learning on Graphs
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Kernel Based Reconstruction for Generalized Graph Signal Processing
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Online Graph Filtering Over Expanding Graphs
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Personalized Graph Signal Processing for Collaborative Filtering
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Graphon Filters: Signal Processing in Very Large Graphs
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Graphon Pooling in Graph Neural Networks
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Weighted sampling and weighted interpolation on combinatorial graphs
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Deep Latent Factor Model for Collaborative Filtering
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Joint Forecasting and Interpolation of Graph Signals Using Deep Learning
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Wide and Deep Graph Neural Networks with Distributed Online Learning
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Robust Graph Learning Under Wasserstein Uncertainty
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