Research And Application Of Blockchain Data Analysis Based On Complex Networks

The development of blockchain technology has sparked a strong interest in cryptocurrencies.As the market value and network fervor of cryptocurrencies continue to rise,cryptocurrencies based on blockchain technology gradually become the choice of people’s investment,and a large number of users participate in accumulating massive transaction data.Compared with traditional financial transaction data,the complete transaction records on the public blockchain and the public nature of the data provide opportunities for researchers to perform data mining and analysis on the on-chain data.Studying the transaction behaviors of mainstream cryptocurrency users helps to better understand the development laws of the virtual economy and the development of blockchain technology itself.The current research on the mainstream cryptocurrency transaction network often stays in the static network or the analysis of a single network,which cannot show the dynamic changes well and neglects the in-depth comparative research on the micro-transaction networks of different cryptocurrencies.Complex network theory has been widely proven to be a powerful tool for modeling and characterizing various complex systems.In this thesis,we will use complex network theory and analysis methods to explore the evolution laws,transaction characteristics,topologies and their dynamics of different cryptocurrency transaction networks,explore and analyze the research content of blockchain data analysis from the perspective of complex networks,and propose methods that can be further studied and applied.The main work of this thesis is as follows.(1)First,combined with the data of Ethereum transactions,we focus on analyzing the transaction data in EOS.IO blockchain,and analyze the data on Ethereum and EOS.IO chain from the perspective of complex network.By constructing cumulative networks with time slices,we build transaction networks of different scales and dynamically analyze the laws of transaction networks over time,and find that many transactions such as transaction volume and transaction relationships show heavy-tailed characteristics and conform to power-law distribution.And it is found that the power-law distribution has temporal invariance with the change of time and the growth of network scale.(2)Second,the similarities and differences in the evolutionary laws and topological characteristics of the microscopic Bitcoin transaction network under conventional and non-conventional conditions are compared and analyzed using some of the daily transaction data from the Mt.Gox exchange and the Elliptic dataset.It is shown that there are significant differences in network metrics between the two different datasets and that both cryptocurrency transaction networks may converge to heavy-tailed distributions in the long run,but their degrees can only be approximated by power-law distributions.As the number of blocks increases,the transaction network degree distribution can be well fitted when the volume of transaction data increases.(3)Finally,this thesis explores the practical applications of blockchain data analysis and designs feasible solutions in the field of blockchain security such as transaction pattern recognition and illegal behavior detection.