mlsanityData to 7 Oct 2026

Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks

arXiv 1704.06803 ↗First posted 22 Apr 2017cs.LG cs.IR cs.NAcited by 88

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Abstract (arXiv)

Matrix completion models are among the most common formulations of recommender systems. Recent works have showed a boost of performance of these techniques when introducing the pairwise relationships between users/items in the form of graphs, and imposing smoothness priors on these graphs. However, such techniques do not fully exploit the local stationarity structures of user/item graphs, and the number of parameters to learn is linear w.r.t. the number of users and items. We propose a novel approach to overcome these limitations by using geometric deep learning on graphs. Our matrix completion architecture combines graph convolutional neural networks and recurrent neural networks to learn meaningful statistical graph-structured patterns and the non-linear diffusion process that generates the known ratings. This neural network system requires a constant number of parameters independent of the matrix size. We apply our method on both synthetic and real datasets, showing that it outperforms state-of-the-art techniques.

Key facts. Cited by 88 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Apr 2017.

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Citing papers per year: 2021: 33; 2022: 22; 2023: 13; 2024: 14; 2025: 5; 2026: 1.

88 papers.

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  1. 2021Dirichlet Energy Constrained Learning for Deep Graph Neural Networkscited by 97
  2. 2022Decoupling the Depth and Scope of Graph Neural Networkscited by 94
  3. 2022A Generalization of ViT/MLP-Mixer to Graphscited by 93
  4. 2021BertGCN: Transductive Text Classification by Combining GCN and BERTcited by 74
  5. 2021On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Featurescited by 62
  6. 2021Graph Learning based Recommender Systems: A Reviewcited by 59
  7. 2024GKAN: Graph Kolmogorov-Arnold Networkscited by 52
  8. 2022PaSca: a Graph Neural Architecture Search System under the Scalable Paradigmcited by 37
  9. 2021Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Studycited by 36
  10. 2024Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendationcited by 33
  11. 2021Network of Tensor Time Seriescited by 32
  12. 2022Spiking Graph Convolutional Networkscited by 26
38 more
  1. 2021Evaluating Deep Graph Neural Networkscited by 19
  2. 2021Graph Neural Networks: Methods, Applications, and Opportunitiescited by 16
  3. 2021Deconvolutional Networks on Graph Datacited by 16
  4. 2021Wide and Deep Graph Neural Network with Distributed Online Learningcited by 14
  5. 2023Confidence-Based Feature Imputation for Graphs with Partially Known Featurescited by 13
  6. 2021Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functionscited by 12
  7. 2022Handling Missing Data via Max-Entropy Regularized Graph Autoencodercited by 12
  8. 2021GLocal-K: Global and Local Kernels for Recommender Systemscited by 11
  9. 2023Graph Tensor Networks: An Intuitive Framework for Designing Large-Scale Neural Learning Systems on Multiple Domainscited by 10
  10. 2021Inductive Matrix Completion Using Graph Autoencodercited by 9
  11. 2022Graph-based Neural Acceleration for Nonnegative Matrix Factorizationcited by 9
  12. 2021Temporal Graph Signal Decompositioncited by 8
  13. 2021Efficient Dynamic Graph Representation Learning at Scalecited by 8
  14. 2024Continuous Product Graph Neural Networkscited by 8
  15. 2021Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparisoncited by 7
  16. 2021Node-Variant Graph Filters in Graph Neural Networkscited by 7
  17. 2021Search For Deep Graph Neural Networkscited by 6
  18. 2021Siamese Attribute-missing Graph Auto-encodercited by 6
  19. 2022Learning Mechanically Driven Emergent Behavior with Message Passing Neural Networkscited by 6
  20. 2022Robust Graph Representation Learning for Local Corruption Recoverycited by 6
  21. 2022Spatiotemporal Propagation Learning for Network-Wide Flight Delay Predictioncited by 6
  22. 2023Automated Camera Calibration via Homography Estimation with GNNscited by 6
  23. 2023NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streamscited by 6
  24. 2021GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passingcited by 5
  25. 2021Planning Spatial Networks with Monte Carlo Tree Searchcited by 5
  26. 2022Matrix Completion with Hierarchical Graph Side Informationcited by 5
  27. 2022Learning Stochastic Graph Neural Networks with Constrained Variancecited by 5
  28. 2022Discovering Representative Attribute-stars via Minimum Description Lengthcited by 5
  29. 2022Adaptive and Implicit Regularization for Matrix Completioncited by 5
  30. 2022Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Poolingcited by 5
  31. 2022Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inferencecited by 4
  32. 2022ODformer: Spatial-Temporal Transformers for Long Sequence Origin-Destination Matrix Forecasting Against Cross Application Scenariocited by 4
  33. 2023Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networkscited by 4
  34. 2023Language Semantic Graph Guided Data-Efficient Learningcited by 4
  35. 2021Wasserstein Graph Neural Networks for Graphs with Missing Attributescited by 3
  36. 2022Budget-aware Few-shot Learning via Graph Convolutional Networkcited by 3
  37. 2022MoReL: Multi-omics Relational Learningcited by 3
  38. 2023Link Prediction without Graph Neural Networkscited by 3

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