Network-based indicators of Bitcoin bubbles

The functioning of the cryptocurrency Bitcoin relies on the open availability of the entire history of its transactions. This makes it a particularly interesting socio-economic system to analyse from the point of view of network science. Here we analyse the evolution of the network of Bitcoin transactions between users. We achieve this by using the complete transaction history from December 5th 2011 to December 23rd 2013. This period includes three bubbles experienced by the Bitcoin price. In particular, we focus on the global and local structural properties of the user network and their variation in relation to the different period of price surge and decline. By analysing the temporal variation of the heterogeneity of the connectivity patterns we gain insights on the different mechanisms that take place during bubbles, and find that hubs (i.e., the most connected nodes) had a fundamental role in triggering the burst of the second bubble. Finally, we examine the local topological structures of interactions between users, we discover that the relative frequency of triadic interactions experiences a strong change before, during and after a bubble, and suggest that the importance of the hubs grows during the bubble. These results provide further evidence that the behaviour of the hubs during bubbles significantly increases the systemic risk of the Bitcoin network, and discuss the implications on public policy interventions

Critical slowing down associated with critical transition and risk of collapse in cryptocurrency

The year 2017 saw the rise and fall of the crypto-currency market, followed by high variability in the price of all crypto-currencies. In this work, we study the abrupt transition in crypto-currency residuals, which is associated with the critical transition (the phenomenon of critical slowing down) or the stochastic transition phenomena. We find that, regardless of the specific crypto-currency or rolling window size, the autocorrelation always fluctuates around a high value, while the standard deviation increases monotonically. Therefore, while the autocorrelation does not display signals of critical slowing down, the standard deviation can be used to anticipate critical or stochastic transitions. In particular, we have detected two sudden jumps in the standard deviation, in the second quarter of 2017 and at the beginning of 2018, which could have served as early warning signals of two majors price collapses that have happened in the following periods. We finally propose a mean-field phenomenological model for the price of crypto-currency to show how the use of the standard deviation of the residuals is a better leading indicator of the collapse in price than the time series' autocorrelation. Our findings represent a first step towards a better diagnostic of the risk of critical transition in the price and/or volume of crypto-currencies.Comment: 14 pages, 5 figures, 1 tabl

Spy Based Analysis of Selfish Mining Attack on Multi-Stage Blockchain

In this paper, we present a selfish mining attack on the multi-stage blockchain proposed by Palash Sarkar. We provide detailed analysis of computational wastage of honest miners and biased rewards achieved by the selfish pool. In our analysis, we introduce a spy inside an honest pool which is a trivial task. Our spy is responsible for leaking the information of the stage mining from the honest pool to the selfish pool. In our analysis, we consider all the possible configurations of mining namely sequential, parallel and pipelining. In all of these configurations, we show through our mathematical equations as to how a selfish miner can succeed in wasting the computation power of the honest miner and how he can influence the reward of mining. For completeness, we provide an algorithm for performing a selfish mining attack on all the scenarios on multi-stage blockchain. To thwart selfish mining on multi-stage blockchain we redesign the original verification algorithm by introducing a new parameter called the crypto-stamp. We present a new algorithm that uses crypto-stamp during the verification process of the mined stages or blocks and is able to detect with high probability whether the stages or blocks were kept private or not

Community Evolution in Bitcoin Investor Networks

The rise of cryptocurrencies is one of the phenomena characterizing the past decade. What sets cryptocurrencies apart from the traditional ones is that no central party is required for enforcing the transaction rules and the transactions are also publicly available. Meanwhile, network analysis tools have become widely popular for explaining the complex world shaped by social interaction. Even though the Complex Networks approach has been used for inspecting Bitcoin, the most widely adapted cryptocurrency, no prior study investigates the dynamics of investor communities in Bitcoin networks. The existing studies mostly focus on directed Bitcoin transfer networks while behavioural synchronization networks have not been sufficiently addressed. This thesis sheds a light on the social aspect of Bitcoin by exploring the dynamics of clusters of investors who time their trades similarly. To conduct such a research, we retrieve the public ledger of Bitcoin transactions and extract over 170 million Bitcoin wallets from the anonymous data. A network of active wallets is formed for each month from 2009 until the end of 2019, and two wallets are connected if their trade timing passes a statistical similarity test. Network analysis tools are used for detecting communities in the formed networks, and community evolution analysis is performed by analyzing the community structure of subsequent monthly networks. Our results show that Bitcoin investor communities are mostly short-lived but some persist for months or even years. We also find out that the long-lived investor communities prefer splitting over merging when it comes to persistence methods. This research not only produces novel information, which is valuable as such, but also lays a solid basis for future studies concerned with the evolution of Bitcoin communities by bringing together best practices of varying disciplines

Комп'ютерні науки та інженерія програмного забезпечення

This volume represents the proceedings of the 1st Student Workshop on Computer Science & Software Engineering (CS&SE@SW 2018), held in Kryvyi Rih, Ukraine, in November 30, 2018. It comprises 20 contributed papers that were carefully peer-reviewed and selected from 25 submissions. The accepted papers present the ideas and early results of master’s and PhD projects.Цей том представляє матеріали 1-го студентського семінару з комп'ютерних наук та інженерії програмного забезпечення (CS&SE@SW 2018), який відбувся у Кривому Розі, Україна, 30 листопада 2018 року. Він включає 20 доповідей, які пройшли ретельне рецензування та були відібрані з 25 подань. Прийняті доповіді представляють ідеї та перші результати магістерських і докторських проектів