Evolution of Bitcoin Trust Communities

Bitcoin, a digital currency facilitated by blockchain technology, enables direct exchange and personal ownership of digital assets, verified through mathematical consensus. This paper explores and analyzes transaction data within the Bitcoin network, with a focus on improving the efficiency of entity recognition methods and identifying illegal transaction patterns. We begin by introducing Bitcoin’s development background, underlying principles, and transaction processes. We then delve into the structure of Bitcoin transaction data and review recent literature on its analysis, summarizing key technologies and research directions. To address the inefficiencies of traditional heuristic entity recognition methods, we propose an innovative solution that establishes entity relationship sets and utilizes active address data. Our approach introduces specific advancements, including a novel algorithm designed to enhance network connectivity and stability, and a centrality aggregation index that outperforms traditional node centrality indices. This algorithm facilitates quick reconnection to previously successful peer nodes, discovers new nodes upon connection loss, and propagates node information across the network for more stable connections. Additionally, it employs a seed node mechanism to expedite network discovery. Our method leverages a core data structure that maintains a list of peers for initial connections, automated through a seed node process. This bootstrapping mechanism allows Bitcoin clients to efficiently connect to the entire Bitcoin network. For implementation and analysis, we utilize NetworkX, a Python package for manipulating and investigating complex networks. We visualize the network structure using the number of transactions or reviews as node size, average review sentiment as node color, review mistrust as edge length, and a force-directed algorithm for node positioning. Our results demonstrate that the first-order aggregation centrality index performs better than the node centrality index, confirming that incorporating more information about first-order correlation attributes around a node enhances the model’s effectiveness. Our proposed model, integrating the centrality aggregation index, achieves a 1% improvement in precision, a 5% improvement in recall, and a 4% improvement in F1 score compared to the original feature set model. We define C as the node centrality feature set, C1 as the first-order aggregated feature set, C2 as the second-order aggregated feature set, and AF as the original feature set. From both model performance and visualization perspectives, the centrality aggregation index enables quick identification of key nodes and enhances the discovery of illegal transaction patterns in the network. By reversing and backtracking the capital flow path, our method can uncover more illegal transaction nodes and provide greater interpretability for the illegal transaction model. Finally, we discuss how to analyze and identify illegal behavior characteristics in Bitcoin transaction data, concluding with an examination of data sources, network construction, and analysis methods. By offering a comprehensive exploration of Bitcoin transaction data and advancing entity recognition methods, this paper provides valuable insights into the evolving landscape of cryptocurrency and blockchain technology. Our proposed innovations result in significant efficiency improvements and enhanced detection of illegal activities within the Bitcoin network.