# Towards Improved Illicit Node Detection with Positive-Unlabelled Learning

arXiv: https://arxiv.org/abs/2303.02462 · HTML: https://mlsanity.com/paper/2303.02462/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 4 Mar 2023.

## Builds on (most cited first, 12 of 12)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Poincar'e Embeddings for Learning Hierarchical Representations](https://mlsanity.com/paper/1705.08039/) (2017)
- [Hyperbolic Graph Neural Networks](https://mlsanity.com/paper/1910.12892/) (2019)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding](https://mlsanity.com/paper/1911.09259/) (2019)
- [Detecting Mixing Services via Mining Bitcoin Transaction Network with Hybrid Motifs](https://mlsanity.com/paper/2001.05233/) (2020)
- [Learning Role-based Graph Embeddings](https://mlsanity.com/paper/1802.02896/) (2018)
- [BitcoinHeist: Topological Data Analysis for Ransomware Detection on the Bitcoin Blockchain](https://mlsanity.com/paper/1906.07852/) (2019)
- [Blockchain is Watching You: Profiling and Deanonymizing Ethereum Users](https://mlsanity.com/paper/2005.14051/) (2020)
- [Topological Anomaly Detection in Dynamic Multilayer Blockchain Networks](https://mlsanity.com/paper/2106.01806/) (2021)
- [Evaluating the Predictive Performance of Positive-Unlabelled Classifiers: a brief critical review and practical recommendations for improvement](https://mlsanity.com/paper/2206.02423/) (2022)

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

- [Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks](https://mlsanity.com/paper/2501.12491/) (2025)
- [Learning from sanctioned government suppliers: A machine learning and network science approach to detecting fraud and corruption in Mexico](https://mlsanity.com/paper/2512.19491/) (2025)

## Abstract (arXiv)

Detecting illicit nodes on blockchain networks is a valuable task for strengthening future regulation. Recent machine learning-based methods proposed to tackle the tasks are using some blockchain transaction datasets with a small portion of samples labeled positive and the rest unlabelled (PU). Albeit the assumption that a random sample of unlabeled nodes are normal nodes is used in some works, we discuss that the label mechanism assumption for the hidden positive labels and its effect on the evaluation metrics is worth considering. We further explore that PU classifiers dealing with potential hidden positive labels can have improved performance compared to regular machine learning models. We test the PU classifiers with a list of graph representation learning methods for obtaining different feature distributions for the same data to have more reliable results.