The pseudonymous nature of Bitcoin has sparked the twin rivaling researches in Bitcoin community, that is, either protecting or attacking anonymity. In spite of this intense battle, the answer to a primary question is absent – Do Bitcoin users themselves care about anonymity? This paper demystifies this doubt via analyzing the Bitcoin transaction graphs with the following three contributions: 1). We outline three representative metrics that can signify whether users concern about anonymity. 2). We examine the collective trend of anonymity concerns from a macroscope. 3). We pay particular attention on critical addresses in a microscope to unveil their anonymity concerns.This paper arrives at both expected conclusions and unexpected surprises. In particular, the expected ones are: rich addresses concern more about anonymity than poor ones. Miner addresses start caring about anonymity when exchange rate soars. Stock addresses never hide their intent of jump-and-dump. The surprises are: the majority of the users show weak concerns on anonymity. One can easily find both hot and cold wallet addresses owned by big organizations.
Marc Jourdan, Sébastien Blandin, Laura Wynter, Pralhad Deshpande
The Bitcoin transaction graph is a public data structure organized as transactions between addresses, each associated with a logical entity. In this work, we introduce a complete probabilistic model of the Bitcoin Blockchain, setting the basis for follow-up AI applications on Bitcoin transactions. We first formulate a set of conditional dependencies induced by the Bitcoin protocol at the block level and derive a corresponding fully observed graphical model of a Bitcoin block. We then extend the model to include hidden entity attributes such as the functional category of the associated logical agent and derive asymptotic bounds on the privacy properties implied by this model. At the network level, we show evidence of complex transaction-to-transaction behavior and present a relevant discriminative model of the agent categories. Performance of both the block-based graphical model and the network-level discriminative model are evaluated on a subset of the public Bitcoin Blockchain.
Rubaiyat Islam, Yoshi Fujiwara, Shinya Kawata, Hiwon Yoon
In a closed economic system like blockchain, the total amount of generated cryptocurrency called bitcoin is conserved and the transaction patterns demonstrate an insight of money flow inside the blockchain. For the last 2 years, bitcoin market has grabbed an immense attention from the investors, technology entrepreneurs and currency enthusiasts. In this paper, we have come up with some findings in our investigation about the bitcoin time-series transaction patterns. We have graphically represented bitcoin’s weekly patterns as a real economic currency that has been minted, stored and exchanged inside the bitcoin blockchain network. We identified outliers’ activities with the help of descriptive statistical analysis. We also demonstrated transaction pattern behavioral change. The main implication of these findings is to understand some stylized facts of the time-series transaction of cryptocurrency-based fully digital financial system. Besides in our analysis, we have shown that the behavioral change of the transaction pattern is capable of explaining the system development events or major historical events that have a network impact.
Eaman Jahani, P. M. Krafft, Yoshihiko Suhara, Esteban Moro · 5 authors
Participants in cryptocurrency markets are in constant communication with each other about the latest coins and news releases. Do these conversations build hype through the contagiousness of excitement, help the community process information, or play some other role? Using a novel dataset from a major cryptocurrency forum, we conduct an exploratory study of the characteristics of online discussion around cryptocurrencies. Through a regression analysis, we find that coins with more information available and higher levels of technical innovation are associated with higher quality discussion. People who talk about "serious" coins tend to participate in discussion displaying signatures of collective intelligence and information processing, while people who talk about "less serious" coins tend to display signatures of hype and naïvety. Interviews with experienced forum members also confirm these quantitative findings. These results highlight the varied roles of discussion in the cryptocurrency ecosystem and suggest that discussion of serious coins may be oriented towards earnest, perhaps more accurate, attempts at discovering which coins are likely to succeed.
We showcase a graph mining tool, BiVA, for visualization and analysis of the Bitcoin network. It enables data exploration, visualization of subgraphs around nodes of interest, and integrates both standard and new algorithms, including a general algorithm for flow based clustering for directed graphs, and other Bitcoin network specific wallet address aggregation mechanisms. The BiVA user interface makes it easy to get started with a basic visualization that gives insights into nodes of interests, and the tool is modular, allowing easy integration of new algorithms. Its functionalities are demonstrated with a case study of extortion of Ashley Madison data breach victims.
The bitcoin price has surged in recent years and it has also exhibited phases of rapid decay. In this paper we address the question to what extent this novel cryptocurrency market can be viewed as a classic or semi-efficient market. Novel and robust tools for estimation of multi-fractal properties are used to show that the bitcoin price exhibits a very interesting multi-scale correlation structure. This structure can be described by a power-law behavior of the variances of the returns as functions of time increments and it can be characterized by two parameters, the volatility and the Hurst exponent. These power-law parameters, however, vary in time. A new notion of generalized Hurst exponent is introduced which allows us to check if the multi-fractal character of the underlying signal is well captured. It is moreover shown how the monitoring of the power-law parameters can be used to identify regime shifts for the bitcoin price. A novel technique for identifying the regimes switches based on a goodness of fit of the local power-law parameters is presented. It automatically detects dates associated with some known events in the bitcoin market place. A very surprising result is moreover that, despite the wild ride of the bitcoin price in recent years and its multi-fractal and non-stationary character, this price has both local power-law behaviors and a very orderly correlation structure when it is observed on its entire period of existence.
The Bitcoin protocol prevents the occurrence of double-spending (DS), i.e. the utilization of the same currency unit more than once. At the same time a DS attack, where more conflicting transactions are generated, might be performed to defraud a user, e.g. a merchant. Therefore, in this work, we propose a model for detecting the presence of conflicting transactions by means of an 'oracle' that polls a subset of nodes of the Bitcoin network. We assume that the latter has a complex structure. So, we investigate the relation between the topology of several complex networks and the optimal amount, and distribution, of a subset of nodes chosen by the oracle for polling. Results show that small-world networks require to poll a smaller amount of nodes than regular networks. In addition, in random topologies, a small number of polled nodes can make a detection system fast and reliable even if the underlying network grows.
Cryptocurrency is a well-developed blockchain technology application that is currently a heated topic throughout the world. The public availability of transaction histories offers an opportunity to analyze and compare different cryptocurrencies. In this paper, we present a dynamic network analysis of three representative blockchain-based cryptocurrencies: Bitcoin, Ethereum, and Namecoin. By analyzing the accumulated network growth, we find that, unlike most other networks, these cryptocurrency networks do not always densify over time, and they are changing all the time with relatively low node and edge repetition ratios. Therefore, we then construct separate networks on a monthly basis, trace the changes of typical network characteristics (including degree distribution, degree assortativity, clustering coefficient, and the largest connected component) over time, and compare the three. We find that the degree distribution of these monthly transaction networks cannot be well fitted by the famous power-law distribution, at the same time, different currency still has different network properties, e.g., both Bitcoin and Ethereum networks are heavy-tailed with disassortative mixing, however, only the former can be treated as a small world. These network properties reflect the evolutionary characteristics and competitive power of these three cryptocurrencies and provide a foundation for future research.
This paper deals with the design of the secure blockchain network framework to prevent damages from an attacker. The decentralized network design called the Blockchain Governance Game is a new hybrid theoretical model and it provides the stochastic game framework to find best strategies towards preparation for preventing a network malfunction by an attacker. Analytically tractable results are obtained by using the fluctuation theory and the mixed strategy game theory. These results enable to predict the moment for operations and deliver the optimal portion of backup nodes to protect the blockchain network. This research helps for whom considers the initial coin offering or launching new blockchain based services with enhancing the security features.
Blockchain technology, which has been known by mostly small technological circles up until recently, is bursting throughout the globe, with a potential economic and social impact that could fundamentally alter traditional financial and social structures. Issuing cryptocurrencies on top of the Blockchain system by startups and private sector companies is becoming a ubiquitous phenomenon, inducing the trading of these crypto-coins among their holders using dedicated exchanges. Apart from being a trading ledger for tokens, Blockchain can also be observed as a social network. Analyzing and modeling the dynamics of the "social signals" of this network can contribute to our understanding of this ecosystem and the forces acting within in. This work is the first analysis of the network properties of the ERC20 protocol compliant crypto-coins' trading data. Considering all trading wallets as a network's nodes, and constructing its edges using buy--sell trades, we can analyze the network properties of the ERC20 network. Examining several periods of time, and several data aggregation variants, we demonstrate that the network displays strong power-law properties. These results coincide with current network theory expectations, however nonetheless, are the first scientific validation of it, for the ERC20 trading data. The data we examined is composed of over 30 million ERC20 tokens trades, performed by over 6.8 million unique wallets, lapsing over a two years period between February 2016 and February 2018.
Alexandre Bovet, Carlo Campajola, Jorge F. Lazo, Francesco Mottes · 10 authors
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.
Bitcoins and Blockchain technologies are attracting the attention of different scientific communities. In addition, their widespread industrial applications and the continuous introduction of cryptocurrencies are also stimulating the attention of the public opinion. The underlying structure of these technologies constitutes one of their core concepts. In particular, they are based on peer-to-peer networks. Accordingly, all nodes lie at the same level, so that there is no place for privileged actors as, for instance, banking institutions in classical financial networks. In this work, we perform a preliminary investigation on two kinds of network, i.e. the Bitcoin network and the Bitcoin Cash network. Notably, we analyze their global structure and we try to evaluate if they are provided with a small-world behavior. Results suggest that the principle known as 'fittest-gets-richer', combined with a continuous increasing of connections, might constitute the mechanism leading these networks to reach their current structure. Moreover, further observations open the way to new investigations into this direction.
Bitcoins and Blockchain technologies are attracting the attention of different scientific communities. In addition, their widespread industrial applications and the continuous introduction of cryptocurrencies are also stimulating the attention of the public opinion. The underlying structure of these technologies constitutes one of their core concepts. In particular, they are based on peer-to-peer networks. Accordingly, all nodes lie at the same level, so that there is no place for privileged actors as, for instance, banking institutions in classical financial networks. In this work, we perform a preliminary investigation on two kinds of network, i.e. the Bitcoin network and the Bitcoin Cash network. Notably, we analyze their global structure and we try to evaluate if they are provided with a small-world behavior. Results suggest that the principle known as 'fittest-gets-richer', combined with a continuous increasing of connections, might constitute the mechanism leading these networks to reach their current structure. Moreover, further observations open the way to new investigations into this direction.
We empirically verify that the market capitalizations of coins and tokens in the cryptocurrency universe follow power-law distributions with significantly different values for the tail exponent falling between 0.5 and 0.7 for coins, and between 1.0 and 1.3 for tokens. We provide a rationale for this, based on a simple proportional growth with birth and death model previously employed to describe the size distribution of firms, cities, webpages, etc. We empirically validate the model and its main predictions, in terms of proportional growth (Gibrat's Law) of the coins and tokens. Estimating the main parameters of the model, the theoretical predictions for the power-law exponents of coin and token distributions are in remarkable agreement with the empirical estimations, given the simplicity of the model. Our results clearly characterize coins as being 'entrenched incumbents' and tokens as an 'explosive immature ecosystem', largely due to massive and exuberant Initial Coin Offering activity in the token space. The theory predicts that the exponent for tokens should converge to 1 in the future, reflecting a more reasonable rate of new entrants associated with genuine technological innovations.
We empirically verify that the market capitalisations of coins and tokens in the cryptocurrency universe follow power-law distributions with significantly different values, with the tail exponent falling between 0.5 and 0.7 for coins, and between 1.0 and 1.3 for tokens. We provide a rationale for this, based on a simple proportional growth with birth & death model previously employed to describe the size distribution of firms, cities, webpages, etc. We empirically validate the model and its main predictions, in terms of proportional growth (Gibrat's law) of the coins and tokens. Estimating the main parameters of the model, the theoretical predictions for the power-law exponents of coin and token distributions are in remarkable agreement with the empirical estimations, given the simplicity of the model. Our results clearly characterize coins as being "entrenched incumbents" and tokens as an "explosive immature ecosystem", largely due to massive and exuberant Initial Coin Offering activity in the token space. The theory predicts that the exponent for tokens should converge to 1 in the future, reflecting a more reasonable rate of new entrants associated with genuine technological innovations.
Stjepan Begušić, Zvonko Kostanjčar, H. Eugene Stanley, Boris Podobnik
Detection of power-law behavior and studies of scaling exponents uncover the characteristics of complexity in many real world phenomena. The complexity of financial markets has always presented challenging issues and provided interesting findings, such as the inverse cubic law in the tails of stock price fluctuation distributions. Motivated by the rise of novel digital assets based on blockchain technology, we study the distributions of cryptocurrency price fluctuations. We consider Bitcoin returns over various time intervals and from multiple digital exchanges, in order to investigate the existence of universal scaling behavior in the tails, and ascertain whether the scaling exponent supports the presence of a finite second moment. We provide empirical evidence on slowly decaying tails in the distributions of returns over multiple time intervals and different exchanges, corresponding to a power-law. We estimate the scaling exponent and find an asymptotic power-law behavior with 2 < α < 2.5 suggesting that Bitcoin returns, in addition to being more volatile, also exhibit heavier tails than stocks, which are known to be around 3. Our results also imply the existence of a finite second moment, thus providing a fundamental basis for the usage of standard financial theories and covariance-based techniques in risk management and portfolio optimization scenarios.
Cryptocurrencies are becoming an attractive asset class and are the focus of recent quantitative research. The joint dynamics of the cryptocurrency market yields information on network risk. Utilizing the adaptive LASSO approach, we build a dynamic network of cryptocurrencies and model the latent communities with a dynamic stochastic blockmodel. We develop a dynamic covariate-assisted spectral clustering method to uniformly estimate the latent group membership of cryptocurrencies consistently. We show that return inter-predictability and crypto characteristics, including hashing algorithms and proof types, jointly determine the crypto market segmentation. Based on this classification result, it is natural to employ eigenvector centrality to identify a cryptocurrency’s idiosyncratic risk. An asset pricing analysis finds that a cross-sectional portfolio with a higher centrality earns a higher risk premium. Further tests confirm that centrality serves as a risk factor well and delivers valuable information content on cryptocurrency markets.
One of the most popular platform based on blockchain technology is Ethereum. Internal activity on this public blockchain is analyzed both from a quantitative and qualitative point of view. In a first part, it is shown that the creation of the Ethereum Alliance consortium has been a game changer in the use of the technology. In a second part, the network robustness against attacks is investigated from a graph point of view, as well as the distribution of internal activity among users. Addresses of great influence were identified, and allowed to formulate conjectures on the current usage of this technology.
In recent years, with the rapid development and popularization of BitCoin, the research of blockchain technology has also shown growth. It has gradually become a new generation of distributed, non-centralized and trust-based technology solution. However, the blockchain operation is expensive and transaction is delayed. Take BitCoin as an example. On the one hand, a block is produced every ten minute. On the other hand, once the new block is generated, it takes a certain time to propagate world wide. The slow speed of propagation determines that BitCoin can not use too small block interval time. Ethereum also faces similar problems, so the concept of uncle block was introduced to reduce blockchain forks. This paper introduces a new tree structure based broadcast propagation routing model, providing a novel method to organize network nodes and message propagation mechanism. In oder to avoid the single node failure problem, the tree cluster routing is proposed. The research shows that the tree based routing can accelerate broadcast convergence time and reduce redundant traffic.
Cüneyt Gürcan Akçora, Matthew Dixon, Yulia R. Gel, Murat Kantarcıoğlu
A key challenge for Bitcoin cryptocurrency holders, such as startups using ICOs to raise funding, is managing their FX risk. Specifically, a misinformed decision to convert Bitcoin to fiat currency could, by itself, cost USD millions. In contrast to financial exchanges, Blockchain based crypto-currencies expose the entire transaction history to the public. By processing all transactions, we model the network with a high fidelity graph so that it is possible to characterize how the flow of information in the network evolves over time. We demonstrate how this data representation permits a new form of microstructure modeling - with the emphasis on the topological network structures to study the role of users, entities and their interactions in formation and dynamics of crypto-currency investment risk. In particular, we identify certain sub-graphs ('chainlets') that exhibit predictive influence on Bitcoin price and volatility, and characterize the types of chainlets that signify extreme losses.