# Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks

arXiv: https://arxiv.org/abs/2306.03647 · HTML: https://mlsanity.com/paper/2306.03647/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 54 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Activated Gradients for Deep Neural Networks](https://mlsanity.com/paper/2107.04228/) (2021)
- [A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization](https://mlsanity.com/paper/2301.10499/) (2023)

## Built on by (most cited first, 2 of 2)

- [DT-GOL: Dual-Track Geometric Online Learning in Nonstationary Environment with Label Delay](https://mlsanity.com/paper/2606.22950/) (2026)
- [Federated Hash Projected Latent Factor Learning](https://mlsanity.com/paper/2606.26192/) (2026)

## Abstract (arXiv)

An Undirected Weighted Network (UWN) is commonly found in big data-related applications. Note that such a network's information connected with its nodes, and edges can be expressed as a Symmetric, High-Dimensional and Incomplete (SHDI) matrix. However, existing models fail in either modeling its intrinsic symmetry or low-data density, resulting in low model scalability or representation learning ability. For addressing this issue, a Proximal Symmetric Nonnegative Latent-factor-analysis (PSNL) model is proposed. It incorporates a proximal term into symmetry-aware and data density-oriented objective function for high representation accuracy. Then an adaptive Alternating Direction Method of Multipliers (ADMM)-based learning scheme is implemented through a Tree-structured of Parzen Estimators (TPE) method for high computational efficiency. Empirical studies on four UWNs demonstrate that PSNL achieves higher accuracy gain than state-of-the-art models, as well as highly competitive computational efficiency.