![]() ![]() To quantify the location of an individual account in the network, we used the Hodge decomposition method and found that the Hodge potential of the account has a significant correlation to its position in the bowtie structure as well as to its net flow of incoming and outgoing money and links, namely the net demand/supply of individual accounts. ![]() Specifically, the bowtie analysis indicates what we refer to as a “walnut” structure with core and upstream/downstream components. The network statistics and structures are examined and shown to be similar to those of a nationwide production network. In this study, we investigate the flow of money among bank accounts possessed by firms in a region by employing an exhaustive list of all the bank transfers in a regional bank in Japan, to clarify how the network of money flow is related to the economic activities of the firms. The evaluation of the tool proves that it helped participants to relate multiple information and discover historical trends of Bitcoin mining. The user study demonstrates that Bitcoin miner participants use the tool to analyze higher-level mining activity rather than mining pool details. To explore the evolution and dynamics of this activity over the long term, I developed a VA tool called MiningVis that integrates mining behavior data with contextual information from Bitcoin statistics and news. Simultaneously, miners strategically chose mining pools to maximize their profit. The empirical analysis of this data revealed that emerging mining pools provided a better incentive to attract miners. I propose a method to extract miners from the transaction data and trace pool hopping behavior. From this assessment, I proposed a VA tool to understand mining activities that ensure data integrity and security on the Bitcoin blockchain. First, I provide a characterization of the past work and research challenges related to VA for blockchains. In this thesis, I make several contributions to the analysis of Bitcoin mining activity. The field of Visual Analytics (VA) has been working on the development of analytical systems that allow humans to interact and gain insights from complex data. The volume and evolving nature of its data pose analysis challenges to explore diverse groups of users and different activities on the network. Over the past ten years, transaction activities in Bitcoin have increased rapidly. It has been used as a medium for payments, investments, and digital wallets that are not controlled by any government or financial institution. We further discuss how one can understand the temporal transitions among the three branches.īitcoin is a pioneer cryptocurrency that records transactions in a public distributed ledger called the blockchain. Using this information, we found that the regime switching among Bal-, In-, Out-branches was presumably brought about by the regular players who are not necessarily dominant and stable in the case of Bitcoin, while such players are simply absent in the case of XRP. By examining the identity and business activity of some regular players in the case of Bitcoin, we can observe different roles of them, namely the players balancing surplus and deficit of cryptoassets (Bal-branch), those accumulating the cryptoassets (In-branch), and those reducing it (Out-branch). ![]() ![]() During the most significant period of one-year starting from the winter of 2017, we discovered the structure of three groups of players in the diagram of flow-weighted frequency, which is common to Bitcoin and XRP in spite of the different nature of the two cryptos. We focus on “regular players” who frequently appear on a weekly basis during a period of time including bubble/crash, and quantify each player’s role with respect to outgoing and incoming flows by defining flow-weighted frequency. We study the relationship between these two important aspects of dynamics, one in the bubble/crash of price and the other in the daily network of crypto, by investigating Bitcoin and XRP. The last decade, on the other hand, has witnessed repeating bubbles and crashes of the price of cryptoassets in exchange markets with fiat currencies and other cryptos. Cryptoassets flow among players as recorded in the ledger of blockchain for all the transactions, comprising a network of players as nodes and flows as edges. ![]()
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