VW-DBG: A Dynamically Evolving Bitcoin Transaction Network Model

Abstract

Exploring the evolution of the transaction network is very important for analyzing anonymous transaction behavior of encrypted currency and avoiding illegal crimes. However, with the rapid growth of cryptocurrency transactions in recent years, the traditional static analysis models tend to ignore parameter changes in the evolution process. To solve this problem, constructing time-varying network model is an effective scheme. The main challenge of turning the massive statically stored transaction data into dynamical sequential network is to design a set of suitable network model. This paper took Bitcoin system as example, combined complex network evolution theory, proposed a weighted variable directed bipartite graph (VW-DBG) model. Initially, we defined the transaction weights and the influence inditcator of nodes, and accordingly introduced the node deletion mechanism to Bitcoin transaction network analysis for the first time. Moreover, information entropy indicators was defined to screen key periods in the evolution. In addition, we analyzed the dynamic and static indicators of real Bitcoin transaction network under different observation time using this model, and revealed the pow-law distribution in evolutionary networks.