# HBTBD: A Heterogeneous Bitcoin Transaction Behavior Dataset for Anti-Money Laundering
## 1 Introduction  
Bitcoin money laundering refers to the illegal use of Bitcoin to transfer funds covertly and obscure the source of those funds, taking advantage of Bitcoin's decentralization and anonymity [1]. The graph neural network (GNN) has emerged in recent years. Researchers prefer GNN methods rather than traditional machine learning methods to study Bitcoin trading networks. These are the advantages:  
(1) Graph modeling capability: Graph neural networks excel at handling graph structures, allowing them to effectively analyze the Bitcoin transaction network. (2) Node embedding learning: Graph neural networks learn compact representations for Bitcoin addresses, enabling tasks like clustering, classification, and prediction. (3) Integrating multi-level information: Graph neural networks seamlessly incorporate various types of information, such as structural and attribute information of each Bitcoin transaction. As the work on Bitcoin transaction detection using GNN has gradually increased, the existing related studies can be mainly divided into three types: random walk, graph convolution network, and graph self-encoding. However, each of these methods comes with its own set of advantages and disadvantages:  
(1) Random walk: It can capture local neighbor relations of nodes but has sampling bias and information loss on sparse graphs. (2) Graph convolution network: It can capture the topology between nodes and fuse the characteristic information of nodes, but it cannot capture the long-distance dependencies in the graph [2], and when dealing with unbalanced datasets, the message transmission mechanism will ignore the illegal nodes [3]. (3) Graph autoencoder: It can handle unsupervised and semi-supervised tasks, but overfitting may occur in cases of scarce samples. As for the perspective of the dataset, many challenges and limitations have emerged, from label loss to anonymity (e.g., certain dataset attribute information is not clear). Another limitation we observe is the lack of a heterogeneous dataset that can be used as a research baseline because we believe that the wallet address and transaction entity are equally important in the Bitcoin transaction network structure. Therefore, based on the Elliptic [4] dataset, we reconstructed the Bitcoin transaction dataset from the perspective of heterogeneous graphs to start our work. At the same time, we provided a de-anonymized Bitcoin transaction hash and Bitcoin wallet address to facilitate researchers to jointly promote related studies together. In summary, we propose a framework for Bitcoin anti-money laundering detection. From the collection of Bitcoin transaction data, the extraction of the metapath in the transaction network, to the model training on GNN, and finally, through the classifier, the accuracy of identifying Bitcoin money laundering behavior is improved. The idea of this framework can be extended to the practical application of Bitcoin regulations because its advantages are the flexibility and information richness of the model. It is convenient to improve the encoder, GNN, and classifier parts of this framework. At the same time, when the Bitcoin wallet address is associated with the Bitcoin transaction entities, it can detect the legitimacy of the Bitcoin transaction entities as well as grasp the legitimacy of the wallet address participating in the transaction, which will be beneficial to the relevant regulatory authorities. In general, our contributions are as follows:  
(1) We constructed the HBTBD (heterogeneous bitcoin transaction behavior dataset), which further enriched the elliptic datasets by collecting historical transaction data on the blockchain.