Abstract: The anonymity inherent in blockchain provides a concealed environment for transactions, and Bitcoin address type detection, which aims to categorize addresses with distinct behavioral patterns, is of great significance for blockchain de-anonymization analysis. Existing methods face three major challenges: (1) the Bitcoin transaction network is large and complex, leading to low training efficiency when directly using graph neural networks; (2) deep graph neural networks are prone to over-smoothing, causing node representations to converge and discriminative ability to decline; (3) most methods rely solely on static graph structures or simple statistical features, ignoring temporal transaction patterns and failing to capture dynamic address behaviors. To address these issues, this paper proposes F-BAC (Filter-enhanced Bitcoin Address Classifier), a Bitcoin address type detection method based on a temporal graph feature network. The method consists of four core steps. First, transaction modeling: each transaction of a labeled address is constructed as an independent homogeneous graph, where nodes are addresses involved in the transaction and edges represent fund flows. To handle the large variance in transaction scales, a fine-grained address aggregation compression strategy is proposed, which divides nodes into four tiers based on their total occurrence frequency in all transaction records and aggregates them accordingly, transforming the original heterogeneous graph into a small-scale homogeneous graph with regular structure. This significantly simplifies topological complexity and lays an effective foundation for subsequent graph representation learning. Second, address feature extraction and augmentation: local features are computed for each address, and statistical features are computed for each aggregated address group. Then, a graph feature network (GFN) is applied to propagate and augment the features through three layers while preserving the original node information at each layer, effectively mitigating the over-smoothing problem. Third, temporal feature optimization: the time-ordered sequence of transaction graph representations of the same address is fed into a filter-enhanced MLP (F-MLP), transformed to the frequency domain via discrete Fourier transform, where a trainable linear filter suppresses obfuscation behaviors and noise, and finally reconstructed to an enhanced temporal signal via inverse Fourier transform. This design significantly improves the model's ability to capture periodic transaction patterns. Fourth, address classification: the filtered and optimized temporal graph representation sequence is input into a classifier to output the address type label, trained under the supervision of the cross-entropy loss function. This paper also constructs a dataset incorporating temporal features, providing an important data foundation for subsequent research. Systematic experiments are conducted on our self-constructed dataset and three public datasets. Ablation studies show that removing the address aggregation module, the GFN module, or the temporal filtering module degrades model performance to varying degrees, validating the necessity of the three strategies. On our self-constructed dataset, F-BAC achieves 96.7% precision, 95.9% recall, 96.3% F1-score, and a macro-averaged F1 of 91.1%. On the BAC dataset, F-BAC achieves an F1-score of 98.4%; on the BATC dataset, 98.0%; and on the Google/CvdxBp dataset, 94.6%. Comparative experiments show that F-BAC outperforms existing methods including BAClassifier, Multi-hop GAT, Balanced-BiEGCN, and MDST-GNN. Furthermore, F-BAC reduces data collection volume by 89.3% compared to the BABD method, and the GFN module saves approximately 30%–45% of training time compared to GCN, significantly reducing training costs. In summary, the proposed F-BAC method effectively alleviates the problems of large graph scale, over-smoothing, missing temporal patterns, and obfuscation interference in Bitcoin address type detection through fine-grained address aggregation, graph feature network enhancement, and temporal frequency-domain filtering. Experiments on multiple datasets validate that F-BAC has good generalization performance for this task.

摘要： 区块链的匿名属性为交易提供了隐蔽环境，比特币地址类型检测旨在分类行为模式各异的地址，对区块链去匿名化分析具有重要意义。现有方法主要面临三方面挑战：一是比特币交易网络规模庞大、结构复杂，直接使用图神经网络训练效率低；二是深层图神经网络易出现过度平滑问题，导致节点表征趋同，判别能力下降；三是多数方法仅依赖静态图结构或简单统计特征，忽略了交易行为中的时序模式，难以有效捕捉地址的动态行为特征。针对上述问题，本文提出一种基于时序图特征网络的比特币地址类型检测方法F-BAC（Filter-enhanced Bitcoin Address Classifier）。该方法包含四个核心步骤。一是交易建模。将标签地址的每笔交易构建为独立的同构图，节点为交易涉及的地址，边为资金流向。为应对不同交易规模差异巨大的问题，提出细粒度的地址聚合压缩策略：根据地址在全体交易记录中出现的总频次将节点划分为四个层级并分别聚合，从而将原始异构图转换为结构规整的小规模同构图。该策略显著简化了拓扑复杂性，为后续图表示学习奠定了有效基础。二是地址特征提取与增广。为每个地址计算局部特征，并对聚合后的每类地址计算统计特征。随后，利用图特征网络（GFN）对特征进行三层传播与增广，同时保留每层网络的原始节点信息，有效缓解过度平滑问题。三是时序特征优化。将同一地址按时间排序的交易图表示序列输入过滤器增强的多层感知机（F-MLP），经离散傅里叶变换转换至频域，利用可训练的线性滤波器抑制混淆行为和行为噪声，最后经逆傅里叶变换重构增强后的时序信号。此设计显著提升了模型对周期性交易模式的捕捉能力。四是地址分类。将过滤优化后的时序图表示序列输入分类器，输出地址类型标签，以交叉熵损失函数监督训练。本文构建了一个包含时序特征的数据集，为后续研究提供了重要数据基础。在自建数据集及三个公开数据集上进行了系统实验。消融实验表明，去除地址聚合模块、GFN模块、时序过滤模块模型性能均有不同程度下降，验证了三项策略的必要性。在自建数据集上，F-BAC取得了96.7%的精确率、95.9%的召回率和96.3%的F1值，宏平均F1达到91.1%。在BAC数据集上，F-BAC的F1值达到98.4%；在BATC数据集上达到98.0%；在Google/CvdxBp数据集上达到94.6%。对比实验显示，F-BAC的表现均优于BAClassifier、Multi-hop GAT、Balanced-BiEGCN和MDST-GNN等现有方法。此外，F-BAC较BABD方法减少89.3%数据采集量，GFN模块相较GCN节省约30%-45%的训练时间，显著降低了训练成本。综上所述，本文提出的F-BAC方法通过细粒度地址聚合、图特征网络增强和时序频域过滤三项策略，有效缓解了比特币地址类型检测中图规模过大、过度平滑、时序模式缺失和混淆干扰等问题。在多个数据集上的实验验证了F-BAC在这一任务上具有良好的泛化性能。