# Large-scale Dynamic Network Representation via Tensor Ring Decomposition

arXiv: https://arxiv.org/abs/2304.08798 · HTML: https://mlsanity.com/paper/2304.08798/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 42 references that are in the map.
- First posted on arXiv 18 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Dynamic Neural Networks: A Survey](https://mlsanity.com/paper/2102.04906/) (2021)
- [Tensor Ring Decomposition](https://mlsanity.com/paper/1606.05535/) (2016)
- [Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius](https://mlsanity.com/paper/1910.04426/) (2019)
- [Hyperspectral Super-Resolution via Coupled Tensor Ring Factorization](https://mlsanity.com/paper/2001.01547/) (2020)

## Abstract (arXiv)

Large-scale Dynamic Networks (LDNs) are becoming increasingly important in the Internet age, yet the dynamic nature of these networks captures the evolution of the network structure and how edge weights change over time, posing unique challenges for data analysis and modeling. A Latent Factorization of Tensors (LFT) model facilitates efficient representation learning for a LDN. But the existing LFT models are almost based on Canonical Polyadic Factorization (CPF). Therefore, this work proposes a model based on Tensor Ring (TR) decomposition for efficient representation learning for a LDN. Specifically, we incorporate the principle of single latent factor-dependent, non-negative, and multiplicative update (SLF-NMU) into the TR decomposition model, and analyze the particular bias form of TR decomposition. Experimental studies on two real LDNs demonstrate that the propose method achieves higher accuracy than existing models.