# Analysis of Cryptocurrency Transactions from a Network Perspective: An Overview

arXiv: https://arxiv.org/abs/2011.09318 · HTML: https://mlsanity.com/paper/2011.09318/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis](https://mlsanity.com/paper/2204.05746/) (2022)
- [Know Your Intent: An Autonomous Multi-Perspective LLM Agent Framework for DeFi User Transaction Intent Mining](https://mlsanity.com/paper/2511.15456/) (2025)
- [Effective Illicit Account Detection on Large Cryptocurrency MultiGraphs](https://mlsanity.com/paper/2309.02460/) (2023)
- [Machine Learning for Blockchain Data Analysis: Progress and Opportunities](https://mlsanity.com/paper/2404.18251/) (2024)
- [Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions](https://mlsanity.com/paper/2412.02408/) (2024)
- [Early Rug Pull Warning for BSC Meme Tokens via Multi-Granularity Wash-Trading Pattern Profiling](https://mlsanity.com/paper/2603.13830/) (2026)

## Abstract (arXiv)

As one of the most important and famous applications of blockchain technology, cryptocurrency has attracted extensive attention recently. Empowered by blockchain technology, all the transaction records of cryptocurrencies are irreversible and recorded in the blocks. These transaction records containing rich information and complete traces of financial activities are publicly accessible, thus providing researchers with unprecedented opportunities for data mining and knowledge discovery in this area. Networks are a general language for describing interacting systems in the real world, and a considerable part of existing work on cryptocurrency transactions is studied from a network perspective. This survey aims to analyze and summarize the existing literature on analyzing and understanding cryptocurrency transactions from a network perspective. Aiming to provide a systematic guideline for researchers and engineers, we present the background information of cryptocurrency transaction network analysis and review existing research in terms of three aspects, i.e., network modeling, network profiling, and network-based detection. For each aspect, we introduce the research issues, summarize the methods, and discuss the results and findings given in the literature. Furthermore, we present the main challenges and several future directions in this area.