mlsanityData to 7 Oct 2026

Network of Tensor Time Series

arXiv 2102.07736 ↗First posted 15 Feb 2021cs.LG cs.AIcited by 32

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Abstract (arXiv)

Co-evolving time series appears in a multitude of applications such as environmental monitoring, financial analysis, and smart transportation. This paper aims to address the following challenges, including (C1) how to incorporate explicit relationship networks of the time series; (C2) how to model the implicit relationship of the temporal dynamics. We propose a novel model called Network of Tensor Time Series, which is comprised of two modules, including Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural Network (TRNN). TGCN tackles the first challenge by generalizing Graph Convolutional Network (GCN) for flat graphs to tensor graphs, which captures the synergy between multiple graphs associated with the tensors. TRNN leverages tensor decomposition to model the implicit relationships among co-evolving time series. The experimental results on five real-world datasets demonstrate the efficacy of the proposed method.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 17 of its 46 references that are in the map. First posted on arXiv 15 Feb 2021.

Builds on

17 of 46 references.

References · 17

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5 more
  1. 2017Geometric Matrix Completion with Recurrent Multi-Graph Neural Networkscited by 88
  2. 2019Predicting Path Failure In Time-Evolving Graphscited by 54
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 6; 2023: 4; 2024: 8; 2025: 4; 2026: 4.

32 papers.

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  1. 2025Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrativecited by 35
  2. 2021Graph Communal Contrastive Learningcited by 34
  3. 2024VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connectionscited by 34
  4. 2023Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoderscited by 32
  5. 2022RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Networkcited by 27
  6. 2024Heterogeneous Contrastive Learning for Foundation Models and Beyondcited by 27
  7. 2021Graph Sanitation with Application to Node Classificationcited by 20
  8. 2021Multiplex Graph Neural Network for Extractive Text Summarizationcited by 19
  9. 2021X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learningcited by 19
  10. 2024BACKTIME: Backdoor Attacks on Multivariate Time Series Forecastingcited by 18
  11. 2021DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Datacited by 17
  12. 2025Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesicscited by 17
20 more
  1. 2022COIN: Co-Cluster Infomax for Bipartite Graphscited by 14
  2. 2024PyG-SSL: A Graph Self-Supervised Learning Toolkitcited by 14
  3. 2022Retrieval Based Time Series Forecastingcited by 13
  4. 2023Reconstructing Graph Diffusion History from a Single Snapshotcited by 13
  5. 2023STERLING: Synergistic Representation Learning on Bipartite Graphscited by 12
  6. 2025Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecastingcited by 12
  7. 2022RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graphcited by 11
  8. 2024Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputationcited by 11
  9. 2022DISCO: Comprehensive and Explainable Disinformation Detectioncited by 9
  10. 2024Dynamic Multi-Network Mining of Tensor Time Seriescited by 8
  11. 2023Learning Gaussian Mixture Representations for Tensor Time Series Forecastingcited by 6
  12. 2022ARIEL: Adversarial Graph Contrastive Learningcited by 5
  13. 2024Automated Contrastive Learning Strategy Search for Time Seriescited by 5
  14. 2024Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Seriescited by 5
  15. 2021Networked Time Series Prediction with Incomplete Data via Generative Adversarial Networkcited by 1
  16. 2026What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditionscited by 1
  17. 2025Tensor Convolutional Network for Higher-Order Interaction Prediction in Sparse Tensorscited by 0
  18. 2026Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learningcited by 0
  19. 2026AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networkscited by 0
  20. 2026Impute On-Demand: Adaptive Correlated Time Series Imputation for Changing Environmentscited by 0