# Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks

arXiv: https://arxiv.org/abs/2208.01923 · HTML: https://mlsanity.com/paper/2208.01923/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 57 references that are in the map.
- First posted on arXiv 3 Aug 2022.

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

- [T-EDGE: Temporal WEighted MultiDiGraph Embedding for Ethereum Transaction Network Analysis](https://mlsanity.com/paper/1905.08038/) (2019)
- [A Survey on Security and Privacy Issues of Bitcoin](https://mlsanity.com/paper/1706.00916/) (2017)
- [Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network](https://mlsanity.com/paper/1902.01941/) (2019)

## Built on by (most cited first, 1 of 1)

- [High-order Graph Neural Networks with Common Neighbor Awareness for Link Prediction](https://mlsanity.com/paper/2504.18758/) (2025)

## Abstract (arXiv)

With the development of blockchain technology, the cryptocurrency based on blockchain technology is becoming more and more popular. This gave birth to a huge cryptocurrency transaction network has received widespread attention. Link prediction learning structure of network is helpful to understand the mechanism of network, so it is also widely studied in cryptocurrency network. However, the dynamics of cryptocurrency transaction networks have been neglected in the past researches. We use graph regularized method to link past transaction records with future transactions. Based on this, we propose a single latent factor-dependent, non-negative, multiplicative and graph regularized-incorporated update (SLF-NMGRU) algorithm and further propose graph regularized nonnegative latent factor analysis (GrNLFA) model. Finally, experiments on a real cryptocurrency transaction network show that the proposed method improves both the accuracy and the computational efficiency