Bitnas: An Integrated TBTN-based System for Bitcoin Trading Behavior Analysis and Evaluation

As blockchain technology gains widespread adoption, the need to analyze blockchain transactions becomes increasingly critical. Bitcoin, the blockchain with the highest market value, has attracted significant attention over the past decade. Among various techniques used for analyzing Bitcoin transactions, the Bitcoin Transaction Network (BTN) model stands out due to its comprehensive feature extraction and efficient transaction analysis capabilities. However, the BTN model suffers limitations, including challenges in constructing transaction subnetworks, difficulties in backward tracing, and time-intensive multi-entity transaction analysis. To address these concerns, this paper introduces a Temporal BTN (TBTN) model, designed to reduce the time overheads incurred during transaction analysis. It also presents a transaction analysis method to track the origins of Bitcoins. Moreover, a multi-entity transaction analysis approach is developed to reduce the analysis complexities and enhance the accuracy of trading impact assessment. These proposed methods are integrated into the Bi tcoin t ransactio n a nalysis s ystem (Bitnas). Theoretical verification and experimental validation based on real-world case studies demonstrate the effectiveness and reliability of the proposed methods in revealing hidden insights from Bitcoin transactions.