Data to 14 Oct 2026

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

arXiv 1612.07659 ↗First posted 22 Dec 2016stat.ML cs.LGcited by 275

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Abstract (arXiv)

This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data. Precisely, GCRN is a generalization of classical recurrent neural networks (RNN) to data structured by an arbitrary graph. Such structured sequences can represent series of frames in videos, spatio-temporal measurements on a network of sensors, or random walks on a vocabulary graph for natural language modeling. The proposed model combines convolutional neural networks (CNN) on graphs to identify spatial structures and RNN to find dynamic patterns. We study two possible architectures of GCRN, and apply the models to two practical problems: predicting moving MNIST data, and modeling natural language with the Penn Treebank dataset. Experiments show that exploiting simultaneously graph spatial and dynamic information about data can improve both precision and learning speed.

Key facts. Cited by 275 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Dec 2016.

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Citing papers per year: 2020: 1; 2021: 48; 2022: 50; 2023: 64; 2024: 51; 2025: 40; 2026: 21.

275 papers.

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2024A Survey on Vision-Language-Action Models for Embodied AIcited by 279
2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 171
2021Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solutioncited by 157
2022ROLAND: Graph Learning Framework for Dynamic Graphscited by 132
2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Modelscited by 118
2021Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learningcited by 106
2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
2021Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networkscited by 95
2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
2021Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Spacecited by 67
2022A Survey on Graph Representation Learning Methodscited by 66

38 more

2022Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observationscited by 64
2021A Review of Graph Neural Networks and Their Applications in Power Systemscited by 63
2022Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Versioncited by 59
2022Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Versioncited by 59
2022Provably expressive temporal graph networkscited by 56
2022Scalable Spatiotemporal Graph Neural Networkscited by 52
2022Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Seriescited by 42
2022Temporal Knowledge Graph Reasoning with Historical Contrastive Learningcited by 42
2023LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?cited by 40
2021Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphscited by 39
2023Taming Local Effects in Graph-based Spatiotemporal Forecastingcited by 37
2023Vision-Based Traffic Accident Detection and Anticipation: A Surveycited by 31
2021Conditional Local Convolution for Spatio-temporal Meteorological Forecastingcited by 30
2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 30
2022Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Modelscited by 29
2022EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphscited by 27
2023Graph Deep Learning for Time Series Forecastingcited by 26
2023Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalizationcited by 26
2021Spatio-temporal graph neural networks for multi-site PV power forecastingcited by 24
2022Sparse Graph Learning from Spatiotemporal Time Seriescited by 22
2022SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learningcited by 22
2022Graph Neural Networks Designed for Different Graph Types: A Surveycited by 21
2023Graph Neural Network-based EEG Classification: A Surveycited by 21
2022Multivariate Time Series Forecasting with Latent Graph Inferencecited by 20
2021Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networkscited by 19
2023Deep learning for dynamic graphs: models and benchmarkscited by 19
2024Spectral Invariant Learning for Dynamic Graphs under Distribution Shiftscited by 19
2023Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stationscited by 18
2023Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detectioncited by 18
2022Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecastingcited by 17
2023Graph-based Time Series Clustering for End-to-End Hierarchical Forecastingcited by 17
2024Graph-based Forecasting with Missing Data through Spatiotemporal Downsamplingcited by 17
2024DyG-Mamba: Continuous State Space Modeling on Dynamic Graphscited by 17
2021TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecastingcited by 16
2023Dynamic Graph Representation Learning with Neural Networks: A Surveycited by 15
2023Feature Transportation Improves Graph Neural Networkscited by 15
2021STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networkscited by 14
2022Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Surveycited by 14

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