mlsanityData to 7 Oct 2026

Graph Tensor Networks: An Intuitive Framework for Designing Large-Scale Neural Learning Systems on Multiple Domains

arXiv 2303.13565 ↗First posted 23 Mar 2023cs.LGcited by 10

Not on the map. Why?

Abstract (arXiv)

Despite the omnipresence of tensors and tensor operations in modern deep learning, the use of tensor mathematics to formally design and describe neural networks is still under-explored within the deep learning community. To this end, we introduce the Graph Tensor Network (GTN) framework, an intuitive yet rigorous graphical framework for systematically designing and implementing large-scale neural learning systems on both regular and irregular domains. The proposed framework is shown to be general enough to include many popular architectures as special cases, and flexible enough to handle data on any and many data domains. The power and flexibility of the proposed framework is demonstrated through real-data experiments, resulting in improved performance at a drastically lower complexity costs, by virtue of tensor algebra.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 12 of its 28 references that are in the map. First posted on arXiv 23 Mar 2023.

Builds on

12 of 28 references.

References · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2015Tensorizing Neural Networkscited by 246
  4. 2015On the Expressive Power of Deep Learning: A Tensor Analysiscited by 97
  5. 2017Geometric Matrix Completion with Recurrent Multi-Graph Neural Networkscited by 88
  6. 2017Tensor-Train Recurrent Neural Networks for Video Classificationcited by 59
  7. 2017Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectivescited by 53
  8. 2019Compression and Interpretability of Deep Neural Networks via Tucker Tensor Layer: From First Principles to Tensor Valued Back-Propagationcited by 9
  9. 2021Tensor-Train Recurrent Neural Networks for Interpretable Multi-Way Financial Forecastingcited by 6
  10. 2019A Class of Doubly Stochastic Shift Operators for Random Graph Signals and their Boundednesscited by 5
  11. 2020Multi-Graph Tensor Networkscited by 5
  12. 2020Recurrent Graph Tensor Networks: A Low-Complexity Framework for Modelling High-Dimensional Multi-Way Sequencecited by 2

Built on by

Cited by year

Citing papers per year: 2024: 9; 2025: 1.

10 papers.

  1. 2024Tensor-view Topological Graph Neural Networkcited by 12
  2. 2024Neural Circuit Diagrams: Robust Diagrams for the Communication, Implementation, and Analysis of Deep Learning Architecturescited by 8
  3. 2024Continuous Product Graph Neural Networkscited by 8
  4. 2024An introduction to graphical tensor notation for mechanistic interpretabilitycited by 7
  5. 2024Conditional Uncertainty Quantification for Tensorized Topological Neural Networkscited by 3
  6. 2024Tensor-Fused Multi-View Graph Contrastive Learningcited by 3
  7. 2024Towards a Categorical Foundation of Deep Learning: A Surveycited by 2
  8. 2024TEAFormers: TEnsor-Augmented Transformers for Multi-Dimensional Time Series Forecastingcited by 1
  9. 2024A quatum inspired neural network for geometric modelingcited by 0
  10. 2025Second-Order Tensorial Partial Differential Equations on Graphscited by 0