Geometric deep learning approach to knot theory
Not on the map. Why?
Abstract (arXiv)
In this paper, we introduce a novel way to use geometric deep learning for knot data by constructing a functor that takes knots to graphs and using graph neural networks. We will attempt to predict several knot invariants with this approach. This approach demonstrates high generalization capabilities.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 14 references that are in the map. First posted on arXiv 26 May 2023.
Builds on
9 of 14 references.
References · 9
- 2017Attention Is All You Needcited by 59,932
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
- 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
- 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 500
- 2020Principal Neighbourhood Aggregation for Graph Netscited by 477
- 2020Learning to Unknotcited by 40
- 2021Learning knot invariants across dimensionscited by 12
- 2023Graph Neural Networks and 3-Dimensional Topologycited by 4
- 2022An application of neural networks to a problem in knot theory and group theory (untangling braids)cited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.