Roman Matzutt, Oliver Hohlfeld, Martin Henze, Robin Rawiel · 6 authors
As transaction fees skyrocket today, blockchains become increasingly expensive, hurting their adoption in broader applications. This work tackles the saving of transaction fees for economic blockchain applications. The key insight is that other than the existing "default'' mode to execute application logic fully on-chain, i.e., in smart contracts, and in fine granularity, i.e., user request per transaction, there are alternative execution modes with advantages in cost-effectiveness. On Ethereum, we propose a holistic middleware platform supporting flexible and secure transaction executions, including off-chain states and batching of user requests. Furthermore, we propose control-plane schemes to adapt the execution mode to the current workload for optimal runtime cost. We present a case study on the institutional accounts (e.g., coinbase.com) intensively sending Ether on Ethereum blockchains. By collecting real-life transactions, we construct workload benchmarks and show that our work saves 18%\sim 47%18%-47% per invocation than the default baseline while introducing 1.81%\sim 16.59%1.81%-16.59% blocks delay.
Damiano Di Francesco Maesa, Andrea Marino, Laura Ricci
BITCOIN is a novel decentralized cryptocurrency system which has recently received a great attention from a wider audience. An interesting and unique feature of this system is that the complete list of all the transactions occurred from its inception is publicly available. This enables the investigation of funds movements to uncover interesting properties of the BITCOIN economy. In this paper we present a set of analyses of the user graph, i.e. the graph obtained by an heuristic clustering of the graph of BITCOIN transactions. Our analyses consider an up-to-date BITCOIN blockchain, as in December 2015, after the exponential explosion of the number of transactions occurred in the last two years. The set of analyses we defined includes, among others, the analysis of the time evolution of BITCOIN network, the verification of the "rich get richer" conjecture and the detection of the nodes which are critical for the network connectivity.
Youngbin Kim, Jun Gi Kim, Wook Kim, Jae Ho Im · 7 authors
This paper proposes a method to predict fluctuations in the prices of cryptocurrencies, which are increasingly used for online transactions worldwide. Little research has been conducted on predicting fluctuations in the price and number of transactions of a variety of cryptocurrencies. Moreover, the few methods proposed to predict fluctuation in currency prices are inefficient because they fail to take into account the differences in attributes between real currencies and cryptocurrencies. This paper analyzes user comments in online cryptocurrency communities to predict fluctuations in the prices of cryptocurrencies and the number of transactions. By focusing on three cryptocurrencies, each with a large market size and user base, this paper attempts to predict such fluctuations by using a simple and efficient method.
Dan McGinn, David Birch, David Akroyd, Miguel Molina-Solana · 6 authors
This work presents a systemic top-down visualization of Bitcoin transaction activity to explore dynamically generated patterns of algorithmic behavior. Bitcoin dominates the cryptocurrency markets and presents researchers with a rich source of real-time transactional data. The pseudonymous yet public nature of the data presents opportunities for the discovery of human and algorithmic behavioral patterns of interest to many parties such as financial regulators, protocol designers, and security analysts. However, retaining visual fidelity to the underlying data to retain a fuller understanding of activity within the network remains challenging, particularly in real time. We expose an effective force-directed graph visualization employed in our large-scale data observation facility to accelerate this data exploration and derive useful insight among domain experts and the general public alike. The high-fidelity visualizations demonstrated in this article allowed for collaborative discovery of unexpected high frequency transaction patterns, including automated laundering operations, and the evolution of multiple distinct algorithmic denial of service attacks on the Bitcoin network.
In January 3, 2009, Satoshi Nakamoto gave rise to the "Bitcoin Block Chain" creating the first block of the chain hashing on his computers central processing unit (CPU). Since then, the hash calculations to mine Bitcoin have been getting more and more complex, and consequently the mining hardware evolved to adapt to this increasing difficulty. Three generations of mining hardware have followed the CPU's generation. They are GPU's, FPGA's and ASIC's generations. This work presents an agent based artificial market model of the Bitcoin mining process and of the Bitcoin transactions. The goal of this work is to model the economy of the mining process, starting from GPU's generation, the first with economic significance. The model reproduces some "stylized facts" found in real time price series and some core aspects of the mining business. In particular, the computational experiments performed are able to reproduce the unit root property, the fat tail phenomenon and the volatility clustering of Bitcoin price series. In addition, under proper assumptions, they are able to reproduce the price peak at the end of November 2013, its next fall in April 2014, the generation of Bitcoins, the hashing capability, the power consumption, and the mining hardware and electrical energy expenses of the Bitcoin network.
In this explorative study, we examine the economy and transaction network of the decentralized digital currency Bitcoin during the first four years of its existence. The objective is to develop insights into the evolution of the Bitcoin economy during this period. For this, we establish and analyze a novel integrated dataset that enriches data from the Bitcoin blockchain with off-network data such as business categories and geo-locations. Our analyses reveal the major Bitcoin businesses and markets. Our results also give insights on the business distribution by countries and how businesses evolve over time. We also show that there is a gambling network that features many very small transactions. Furthermore, regional differences in the adoption and business distribution could be found. In the network analysis, the small world phenomenon is investigated and confirmed for several subgraphs of the Bitcoin network.
The early Internet witnessed the flourishing of a digitally networked public sphere in which many people, including dissidents who had little to no access to mass media, found a voice as well as a place to connect with one another. As the Internet matures, its initial decentralized form has been increasingly replaced by a small number of ad-financed platforms, such as Facebook and Google, which structure the online experience of billions of people. These platforms often design, control, influence, and “optimize” the user experience according to their own internal values and priorities, sometimes using emergent methods such as algorithmic filtering and computational inference of private traits from computational social science. The shift to a small number of controlling platforms stems from a variety of dynamics, including network effects and the attractions of easier-to-use, closed platforms. This article considers these developments and their consequences for the vitality of the public sphere.
Bitcoin is a rising digital currency and exemplifies the grow- ing need for systematically gathering and analyzing pub- lic transaction data sets such as the blockchain. However, the blockchain in its raw form is just a large ledger listing transfers of currency units between alphanumeric character strings, without revealing contextually relevant real-world information. In this demo, we present GraphSense, which is a solution that applies a graph-centric perspective on digital currency transactions. It allows users to explore transactions and follow the money ow, facilitates analytics by semantically enriching the transaction graph, supports path and graph pattern search, and guides analysts to anomalous data points. To deal with the growing volume and velocity of transaction data, we implemented our solution on a horizontally scalable data processing and analytics infrastructure. Given the ongoing digital transformation in financial services and technologies, we believe that our approach contributes to development of analytics solutions for digital currency ecosystems, which is relevant in fields such as financial analytics, law enforcement, or scientific research
Abstract A multidimensional financial system could provide benefits for individuals, companies, and states. Instead of top-down control, which is destined to eventually fail in a hyperconnected world, a bottom-up creation of value can unleash creative potential and drive innovations. Multiple currency dimensions can represent different externalities and thus enable the design of incentives and feedback mechanisms that foster the ability of complex dynamical systems to self-organize and lead to a more resilient society and sustainable economy. Modern information and communication technologies play a crucial role in this process, as Web 2.0 and online social networks promote cooperation and collaboration on unprecedented scales. Within this contribution, we discuss how one dimension of a multidimensional currency system could represent socio-digital capital (Social Bitcoins) that can be generated in a bottom-up way by individuals who perform search and navigation tasks in a future version of the digital world. The incentive to mine Social Bitcoins could sustain digital diversity, which mitigates the risk of totalitarian control by powerful monopolies of information and can create new business opportunities needed in times where a large fraction of current jobs is estimated to disappear due to computerization.
Johannes Göbel, Paul Keeler, A. E. Krzesinski, Peter Taylor
In the context of the `selfish-mine' strategy proposed by Eyal and Sirer, we study the effect of propagation delay on the evolution of the Bitcoin blockchain. First, we use a simplified Markov model that tracks the contrasting states of belief about the blockchain of a small pool of miners and the `rest of the community' to establish that the use of block-hiding strategies, such as selfish-mine, causes the rate of production of orphan blocks to increase. Then we use a spatial Poisson process model to study values of Eyal and Sirer's parameter $γ$, which denotes the proportion of the honest community that mine on a previously-secret block released by the pool in response to the mining of a block by the honest community. Finally, we use discrete-event simulation to study the behaviour of a network of Bitcoin miners, a proportion of which is colluding in using the selfish-mine strategy, under the assumption that there is a propagation delay in the communication of information between miners.
This study analyses the growing area of research that explores the evolution of technology from social and cognition perspective – and how the design and various implementation of technology are being shaped by the factors related to social-constructivism and beliefs systems of individuals. The newly developed technological phenomena of Cryptocurrency – the digital currency for all, provides us with an excellent case to study. We apply social and cognitive processes to understand technology trajectories across the life cycle of cryptocurrency. We thus deepen our understanding by analyzing why and what causes the various technological trajectories in the era of ferment and concluding our research by deriving various technological 'themes'. – that might evolve as the phenomena of cryptocurrency while moving towards the era of dominant design.
The role of Bitcoin -open source virtual peer-to-peer money -in finance has become more important with the increasing acceptance by service providers. Nevertheless several financial institutes and governments explain their revulsion against Bitcoin, due to the unknown financial risks behind it which could have an impact on the global financial world. In this paper we examine the relationship between BTC/USD exchange rate and the network properties of the underlying transactional graph. The main goal of our research is to get a deeper understanding on the behavior of Bitcoin and ground further researches on exploring the financial risk. To characterize the transactional graph network analysis techniques, while to examine the relationship data mining and time series analysis techniques were used.
Bit coin, as the foundation for a secure electronic payment system, has drawn broad interests from researchers in recent years. In this paper, we analyze a comprehensive Bit coin transaction dataset and investigate the interrelationship between the flow of Bit coin transactions and its price movement. Using network theory, we examine a few complexity measures of the Bit coin transaction flow networks, and we model the joint dynamic relationship between these complexity measures and Bit coin market variables such as return and volatility. We find that a particular complexity measure of the Bit coin transaction network flow is significantly correlated with the Bit coin market return and volatility. More specifically we document that the residual diversity or freedom of Bit coin network flow scaled by the total system throughput can significantly improve the predictability of Bit coin market return and volatility.
Digital currencies represent a new method for exchange and investment that differs strongly from any other fiat money seen throughout history. A digital currency makes it possible to perform all financial transactions without the intervention of a third party to act as an arbiter of verification; payments can be made between two people with degrees of anonymity, across continents, at any denomination, and without any transaction fees going to a central authority. The most successful example of this is Bitcoin, introduced in 2008, which has experienced a recent boom of popularity, media attention, and investment. With this surge of attention, we became interested in finding out how people both inside and outside the Bitcoin community perceive Bitcoin -- what do they think of it, how do they feel, and how knowledgeable they are. Towards this end, we conducted the first interview study (N = 20) with participants to discuss Bitcoin and other related financial topics. Some of our major findings include: not understanding how Bitcoin works is not a barrier for entry, although non-user participants claim it would be for them and that user participants are in a state of cognitive dissonance concerning the role of governments in the system. Our findings, overall, contribute to knowledge concerning Bitcoin and attitudes towards digital currencies in general.
Bitcoin is an emerging crypto-currency, which is wrapped in mystery and controversy. The goal is to transform how we transfer payments. The current approach for sending money from one remote party to another is via bank deposit and transfer by check or bank transfer. PayPal and other services were developed to provide faster payments to verified individuals, but each layer in the transaction adds time, cost, and/or risk to the transaction. Users of this new digital currency proclaim the benefits of security, anonymity, and efficiency for making transactions. The functionality and structure of the Bitcoin Network is complex and often attacked for not being a suitable replacement for currency. An independent understanding can be developed of the composite Bitcoin Financial Systems of Systems architecture by considering the challenges any System of System would face. A functional analysis, employing the Systems Modeling Language (SysML), is performed on the Bitcoin System of Systems architecture to help gain an understanding of the structure and functionality, and how that relates to the key actors and use cases, for determining if the users’ expectations are aligned with the architecture.
Martina Matta, Maria Ilaria Lunesu, Michele Marchesi
In the last decade, Web 2.0 services such as blogs, tweets, forums, chats, email etc. have been widely used as communication media, with very good results. Sharing knowledge is an important part of learning and enhancing skills. Furthermore, emotions may affect decisionmaking and individual behavior. Bitcoin, a decentralized electronic currency system, represents a radical change in financial systems, attracting a large number of users and a lot of media attention. In this work, we investigated if the spread of the Bitcoin’s price is related to the volumes of tweets or Web Search media results. We compared trends of price with Google Trends data, volume of tweets and particularly with those that express a positive sentiment. We found significant cross correlation values, especially between Bitcoin price and Google Trends data, arguing our initial idea based on studies about trends in stock and goods market.
Dániel Kondor, István Csabai, János Szüle, Márton Pósfai · 5 authors
A main focus in economics research is understanding the time series of prices of goods and assets. While statistical models using only the properties of the time series itself have been successful in many aspects, we expect to gain a better understanding of the phenomena involved if we can model the underlying system of interacting agents. In this article, we consider the history of Bitcoin, a novel digital currency system, for which the complete list of transactions is available for analysis. Using this dataset, we reconstruct the transaction network between users and analyze changes in the structure of the subgraph induced by the most active users. Our approach is based on the unsupervised identification of important features of the time variation of the network. Applying the widely used method of Principal Component Analysis to the matrix constructed from snapshots of the network at different times, we are able to show how structural changes in the network accompany significant changes in the exchange price of bitcoins.
David García, Claudio J. Tessone, Pavlin Mavrodiev, Nicolas Perony
What is the role of social interactions in the creation of price bubbles? Answering this question requires obtaining collective behavioural traces generated by the activity of a large number of actors. Digital currencies offer a unique possibility to measure socio-economic signals from such digital traces. Here, we focus on Bitcoin, the most popular cryptocurrency. Bitcoin has experienced periods of rapid increase in exchange rates (price) followed by sharp decline; we hypothesise that these fluctuations are largely driven by the interplay between different social phenomena. We thus quantify four socio-economic signals about Bitcoin from large data sets: price on on-line exchanges, volume of word-of-mouth communication in on-line social media, volume of information search, and user base growth. By using vector autoregression, we identify two positive feedback loops that lead to price bubbles in the absence of exogenous stimuli: one driven by word of mouth, and the other by new Bitcoin adopters. We also observe that spikes in information search, presumably linked to external events, precede drastic price declines. Understanding the interplay between the socio-economic signals we measured can lead to applications beyond cryptocurrencies to other phenomena which leave digital footprints, such as on-line social network usage.
Dániel Kondor, Márton Pósfai, István Csabai, Gábor Vattay
The possibility to analyze everyday monetary transactions is limited by the scarcity of available data, as this kind of information is usually considered highly sensitive. Present econophysics models are usually employed on presumed random networks of interacting agents, and only macroscopic properties (e.g. the resulting wealth distribution) are compared to real-world data. In this paper, we analyze BitCoin, which is a novel digital currency system, where the complete list of transactions is publicly available. Using this dataset, we reconstruct the network of transactions, and extract the time and amount of each payment. We analyze the structure of the transaction network by measuring network characteristics over time, such as the degree distribution, degree correlations and clustering. We find that linear preferential attachment drives the growth of the network. We also study the dynamics taking place on the transaction network, i.e. the flow of money. We measure temporal patterns and the wealth accumulation. Investigating the microscopic statistics of money movement, we find that sublinear preferential attachment governs the evolution of the wealth distribution. We report a scaling relation between the degree and wealth associated to individual nodes.
Abstract. We study the probability that two or more agents can attain common knowledge of nontrivial events when the size of the state space grows large. We adopt the standard epistemic model where the knowledge of an agent is represented by a partition of the state space. Each agent is endowed with a partition generated by a random scheme consistent with his cognitive capacity. Assuming that agents ’ partitions are independently distributed, we prove that the asymptotic probability of nontrivial common knowledge undergoes a phase transition. Regardless of the number of agents, when their cognitive capacity is sufficiently large, the probability goes to one; and when it is small, it goes to zero. Our proofs rely on a graph-theoretic characterization of common knowledge that has independent interest.
Abstract. The Bitcoin scheme is a rare example of a large scale global payment system in which all the transactions are publicly accessible (but in an anonymous way). We downloaded the full history of this scheme, and analyzed many statistical properties of its associated transaction graph. In this paper we answer for the first time a variety of interesting questions about the typical behavior of users, how they acquire and how they spend their bitcoins, the balance of bitcoins they keep in their accounts, and how they move bitcoins between their various accounts in order to better protect their privacy. In addition, we isolated all the large transactions in the system, and discovered that almost all of them are closely related to a single large transaction that took place in November 2010, even though the associated users apparently tried to hide this fact with many strange looking long chains and fork-merge structures in the transaction graph.