# Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensics arXiv: https://arxiv.org/abs/2306.06108 ยท HTML: https://mlsanity.com/paper/2306.06108/ ## Key facts - Cited by 19 papers in the map (data through 14 Oct 2026). - Builds on 22 of its 49 references that are in the map. - First posted on arXiv 25 May 2023. ## Builds on (most cited first, 22 of 22) - [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016) - [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017) - [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016) - [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) - [Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity](https://mlsanity.com/paper/2005.14635/) (2020) - [T-EDGE: Temporal WEighted MultiDiGraph Embedding for Ethereum Transaction Network Analysis](https://mlsanity.com/paper/1905.08038/) (2019) - [TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection](https://mlsanity.com/paper/2204.13442/) (2022) - [Data mining for detecting Bitcoin Ponzi schemes](https://mlsanity.com/paper/1803.00646/) (2018) - [Behavior-aware Account De-anonymization on Ethereum Interaction Graph](https://mlsanity.com/paper/2203.09360/) (2022) - [GuiltyWalker: Distance to illicit nodes in the Bitcoin network](https://mlsanity.com/paper/2102.05373/) (2021) - [Inspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoin](https://mlsanity.com/paper/2203.10465/) (2022) - [Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods](https://mlsanity.com/paper/1611.03941/) (2016) - [Modeling and Understanding Ethereum Transaction Records via a Complex Network Approach](https://mlsanity.com/paper/2012.15462/) (2020) - [Identity Inference on Blockchain using Graph Neural Network](https://mlsanity.com/paper/2104.06559/) (2021) - [Characterizing and Detecting Money Laundering Activities on the Bitcoin Network](https://mlsanity.com/paper/1912.12060/) (2019) - [Cascading Machine Learning to Attack Bitcoin Anonymity](https://mlsanity.com/paper/1910.06560/) (2019) - [Detecting Malicious Accounts in Permissionless Blockchains using Temporal Graph Properties](https://mlsanity.com/paper/2007.05169/) (2020) - [Anomaly Detection in the Bitcoin System - A Network Perspective](https://mlsanity.com/paper/1611.03942/) (2016) - [Deanonymisation of clients in Bitcoin P2P network](https://mlsanity.com/paper/1405.7418/) (2014) - [MonLAD: Money Laundering Agents Detection in Transaction Streams](https://mlsanity.com/paper/2201.10051/) (2022) ## Built on by (most cited first, 19 of 19) - [Explain First, Trust Later: LLM-Augmented Explanations for Graph-Based Crypto Anomaly Detection](https://mlsanity.com/paper/2506.14933/) (2025) - [ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks](https://mlsanity.com/paper/2508.20829/) (2025) - [A Multi-Scale Graph Learning Framework with Temporal Consistency Constraints for Financial Fraud Detection in Transaction Networks under Non-Stationary Conditions](https://mlsanity.com/paper/2603.14592/) (2026) - [Slow is Fast! Dissecting Ethereum's Slow Liquidity Drain Scams](https://mlsanity.com/paper/2503.04850/) (2025) - [Interpreting Graph Inference with Skyline Explanations](https://mlsanity.com/paper/2505.07635/) (2025) - [Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection](https://mlsanity.com/paper/2506.21382/) (2025) - [Detecting Fraud in Financial Networks: A Semi-Supervised GNN Approach with Granger-Causal Explanations](https://mlsanity.com/paper/2507.01980/) (2025) - [Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions](https://mlsanity.com/paper/2509.07392/) (2025) - [A Depth-Aware Comparative Study of Euclidean and Hyperbolic Graph Neural Networks on Bitcoin Transaction Systems](https://mlsanity.com/paper/2603.16080/) (2026) - [Do Transaction-Level and Actor-Level AML Queues Agree? An Empirical Evaluation of Granularity Effects on the Elliptic++ Graph](https://mlsanity.com/paper/2604.23494/) (2026) - [When Can Fraud Operations Authorize Automation? A Decision-Support Framework for Fresh Audit Evidence and Review Workload](https://mlsanity.com/paper/2608.08577/) (2026) - [MDTE: Minority-Aware Diffusion over Temporal Edge Events for Imbalanced Node Classification](https://mlsanity.com/paper/2608.24812/) (2026) - [Population-Calibrated Graph Screening at 835-Million-Address Scale, with Label-Free Transfer to New Chains](https://mlsanity.com/paper/2609.03036/) (2026) - [ETH-TraceBench: A Large-Scale Event-Stream Benchmark for Ethereum DeFi under Temporal, Protocol, and Contract Shift](https://mlsanity.com/paper/2609.23659/) (2026) - [DynGraphAgentBench: A Benchmark for Agentic Lifecycle Control in Dynamic Graph Anomaly Detection](https://mlsanity.com/paper/2609.33980/) (2026) - [Behavior-Grounded Semantic Enrichment for Financial Fraud Modeling and Reasoning](https://mlsanity.com/paper/2609.34211/) (2026) - [Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes](https://mlsanity.com/paper/2609.38424/) (2026) - [Discrete Diffusion for Large Graph Generation via Structural Candidate Restriction](https://mlsanity.com/paper/2610.04056/) (2026) - [CryptoFlowBench: Benchmarking Multi-Hop Reasoning on Blockchains for Cryptocurrency Fund Tracing](https://mlsanity.com/paper/2610.16449/) (2026) ## Abstract (arXiv) Blockchain provides the unique and accountable channel for financial forensics by mining its open and immutable transaction data. A recent surge has been witnessed by training machine learning models with cryptocurrency transaction data for anomaly detection, such as money laundering and other fraudulent activities. This paper presents a holistic applied data science approach to fraud detection in the Bitcoin network with two original contributions. First, we contribute the Elliptic++ dataset, which extends the Elliptic transaction dataset to include over 822k Bitcoin wallet addresses (nodes), each with 56 features, and 1.27M temporal interactions. This enables both the detection of fraudulent transactions and the detection of illicit addresses (actors) in the Bitcoin network by leveraging four types of graph data: (i) the transaction-to-transaction graph, representing the money flow in the Bitcoin network, (ii) the address-to-address interaction graph, capturing the types of transaction flows between Bitcoin addresses, (iii) the address-transaction graph, representing the bi-directional money flow between addresses and transactions (BTC flow from input address to one or more transactions and BTC flow from a transaction to one or more output addresses), and (iv) the user entity graph, capturing clusters of Bitcoin addresses representing unique Bitcoin users. Second, we perform fraud detection tasks on all four graphs by using diverse machine learning algorithms. We show that adding enhanced features from the address-to-address and the address-transaction graphs not only assists in effectively detecting both illicit transactions and illicit addresses, but also assists in gaining in-depth understanding of the root cause of money laundering vulnerabilities in cryptocurrency transactions and the strategies for fraud detection and prevention. Released at github.com/git-disl/EllipticPlusPlus.