Blockchain Papers

John McCallig, Alastair Robb, Fiona Rohde

No abstract is available for this record.

Tomaso Aste

We study the dependency and causality structure of the cryptocurrency market investigating collective movements of both prices and social sentiment related to almost two thousand cryptocurrencies traded during the first six months of 2018. This is the first study of the whole cryptocurrency market structure. It introduces several rigorous innovative methodologies applicable to this and to several other complex systems where a large number of variables interact in a non-linear way, which is a distinctive feature of the digital economy. The analysis of the dependency structure reveals that prices are significantly correlated with sentiment. The major, most capitalised cryptocurrencies, such as bitcoin, have a central role in the price correlation network but only a marginal role in the sentiment network and in the network describing the interactions between the two. The study of the causality structure reveals a causality network that is consistently related with the correlation structures and shows that both prices cause sentiment and sentiment cause prices across currencies with the latter being stronger in size but smaller in number of significative interactions. Overall our study uncovers a complex and rich structure of interrelations where prices and sentiment influence each other both instantaneously and with lead-lag causal relations. A major finding is that minor currencies, with small capitalisation, play a crucial role in shaping the overall dependency and causality structure. Despite the high level of noise and the short time-series we verified that these networks are significant with all links statistically validated and with a structural organisation consistently reproduced across all networks.

Muhammad Saad, Victor Cook, Lan N. Nguyen, My T. Thai · 5 authors

In this paper, we explore the partitioning attacks on the Bitcoin network, which is shown to exhibit spatial bias, and temporal and logical diversity. Through data-driven study we highlight: 1) the centralization of Bitcoin nodes across autonomous systems, indicating the possibility of BGP attacks, 2)the non-uniform consensus among nodes, that can be exploited to partition the network, and 3)the diversity in the Bitcoin software usage that can lead to privacy attacks. Atop the prior work, which focused on spatial partitioning, our work extends the analysis of the Bitcoin network to understand the temporal and logical effects on the robustness of the Bitcoin network.

Weili Chen, Jun Wu, Zibin Zheng, Chuan Chen · 5 authors

The cryptocurrency market is a very huge market without effective\nsupervision. It is of great importance for investors and regulators to\nrecognize whether there are market manipulation and its manipulation patterns.\nThis paper proposes an approach to mine the transaction networks of exchanges\nfor answering this question.By taking the leaked transaction history of Mt. Gox\nBitcoin exchange as a sample,we first divide the accounts into three categories\naccording to its characteristic and then construct the transaction history into\nthree graphs. Many observations and findings are obtained via analyzing the\nconstructed graphs. To evaluate the influence of the accounts' transaction\nbehavior on the Bitcoin exchange price,the graphs are reconstructed into series\nand reshaped as matrices. By using singular value decomposition (SVD) on the\nmatrices, we identify many base networks which have a great correlation with\nthe price fluctuation. When further analyzing the most important accounts in\nthe base networks, plenty of market manipulation patterns are found. According\nto these findings, we conclude that there was serious market manipulation in\nMt. Gox exchange and the cryptocurrency market must strengthen the supervision.\n

Weili Chen, Jun Wu, Zibin Zheng, Chuan Chen · 5 authors

The cryptocurrency market is a very huge market without effective supervision. It is of great importance for investors and regulators to recognize whether there are market manipulation and its manipulation patterns. This paper proposes an approach to mine the transaction networks of exchanges for answering this question. By taking the leaked transaction history of Mt. Gox Bitcoin exchange as a sample, we first divide the accounts into three categories according to its characteristic and then construct the transaction history into three graphs. Many observations and findings are obtained via analyzing the constructed graphs. To evaluate the influence of the accounts' transaction behavior on the Bitcoin exchange price, the graphs are reconstructed into series and reshaped as matrices. By using singular value decomposition (SVD) on the matrices, we identify many base networks which have a great correlation with the price fluctuation. When further analyzing the most important accounts in the base networks, plenty of market manipulation patterns are found. According to these findings, we conclude that there was serious market manipulation in Mt. Gox exchange and the cryptocurrency market must strengthen the supervision.

István András Seres, László Gulyás, Dániel Nagy, Péter Burcsi

Bitcoin's Lightning Network (LN) is a scalability solution for Bitcoin allowing transactions to be issued with negligible fees and settled instantly at scale. In order to use LN, funds need to be locked in payment channels on the Bitcoin blockchain (Layer-1) for subsequent use in LN (Layer-2). LN is comprised of many payment channels forming a payment channel network. LN's promise is that relatively few payment channels already enable anyone to efficiently, securely and privately route payments across the whole network. In this paper, we quantify the structural properties of LN and argue that LN's current topological properties can be ameliorated in order to improve the security of LN, enabling it to reach its true potential.

Manlio De Domenico, Andrea Baronchelli

The blockchain technology promises to transform finance, money and even governments. However, analyses of blockchain applicability and robustness typically focus on isolated systems whose actors contribute mainly by running the consensus algorithm. Here, we highlight the importance of considering trustless platforms within the broader ecosystem that includes social and communication networks. As an example, we analyse the flash-crash observed on 21st June 2017 in the Ethereum platform and show that a major phenomenon of social coordination led to a catastrophic cascade of events across several interconnected systems. We propose the concept of ``emergent centralisation'' to describe situations where a single system becomes critically important for the functioning of the whole ecosystem, and argue that such situations are likely to become more and more frequent in interconnected socio-technical systems. We anticipate that the systemic approach we propose will have implications for future assessments of trustless systems and call for the attention of policy-makers on the fragility of our interconnected and rapidly changing world.

Z. Mierzwa, З. Межва

The paper deals with the problems of measuring uneven wealth distribution in the bitcoin ecosystem. All existing bitcoin distribution models depend on the analysis of bitcoin wallets and bitcoin addresses. They are based on the Bitcoin Rich List. This approach is insufficient due to the inscrutable relationships between people owning bitcoin, bitcoin wallets, and bitcoin addresses. In this paper, we used the methods of comparative analysis resulted in graphics as represented by Lorentz and Lame curves and distribution of the Gini coefficients and the Kolkata index. We identified empirical cumulative functions of wealth distribution and the number of addresses with positive balance during the bubble and after its explosion. Approximations of the distribution of ‘poor’ and ‘rich’ addresses have been obtained and compared with the other results from the cited literature. The general public views the equality of network members as synonymous with the equal distribution of wealth among them. Emerging financial bubbles, especially in the US financial markets, lead to an increase in income inequality. However, after a bubble explodes, the inequality falls to the initial level. В статье рассматриваются проблемы измерения неравномерности распределения богатства в экосистеме биткоин. Все существующие модели распределения биткоин зависят от анализа биткоин-кошельков и биткоин-адресов. Они основаны на богатом списке биткоинов. Такого подхода недостаточно из-за непостижимых отношений между людьми, владеющими биткоинами, биткоин-кошельками и биткоин-адресами. В работе нами использовались методы сравнительного анализа с графическим изображением результатов в виде кривых Лоренца, Ламе и распределения коэффициентов Джини и индекса Кольката. Авторы определили эмпирические кумулятивные функции распределения богатства и количества адресов с положительным балансом во время пузыря и после его взрыва. Получены аппроксимации распределения «бедных» и «богатых» адресов и сделано их сравнение с другими результатами, представленными в цитируемой литературе. Широкая общественность рассматривает равенство членов сети как синоним относительно равного распределения богатства между ними. Появление финансовых пузырей, в особенности на финансовых рынках США, приводит к увеличению неравенства доходов, но после краха пузыря неравенство падает до начального уровня

Simon Albrecht, Bernhard Lutz, Dirk Neumann

The blockchain technology is subject to intensive discourse on social media. One example is the promotion of Initial Coin Offerings (ICO) as a new financial instrument for startups. As ICOs are generally low-regulated, potential investors and entrepreneurs are faced with decision-making under uncertainty. This uncertainty might be partially offset by social factors. Based on established behavioral, economic and information systems theory, we investigate how different social media channels are linked to the capitalization of blockchain startups. We present empirical evidence from online search trends, financial data and a corpus of 231,758 tweets related to 524 ICOs. Our results suggest that higher search volume, positive sentiment and the increased use of emotive language on Twitter are linked to a high capitalization. The results are relevant for startups and potential ICO investors.

Seonggeun Kim, S. Hahn

In the current blockchain network, many participants rationally migrate the pool to receive a better compensation according to their contribution in situations where the pools they engage encounter undesirable attacks. The Nash equilibria of attacked pool has been widely analyzed, but the analysis of practical methodology for obtaining it is still inadequate. In this paper, we propose an evolutionary game theoretic analysis of Proof-of-Work (PoW) based blockchain network in order to investigate the mining pool dynamics affected by malicious infiltrators and the feasibility of autonomous migration among individual miners. We formulate a revenue model for mining pools which are implicitly allowed to launch a block withholding attack. Under our mining game, we analyze the evolutionary stability of Nash equilibrium with replicator dynamics, which can explain the population change with time between participated pools. Further, we explore the statistical approximation of successful mining events to show the necessity of artificial manipulation for migrating. Finally, we construct a better response learning based on the required block size which can lead to our evolutionarily stable strategy (ESS) with numerical results that support our theoretical discoveries.

Bai Chong

No abstract is available for this record.

Mohamed Torky, Emad Nabil, Wael Said

Rumors and misleading information detection and prevention still represent a big challenge against social network developers and researchers. Since newsworthy information propagation is a traditional behavior of most of the users in social media, then verifying information credibility and reliability is indeed a vital security requirement for social network platforms. Due to its immutability, security, tamper-proof and P2P design, Blockchain as a powerful technology can provide a magical solution to overcome this challenge. This Paper introduces a novel blockchain approach called Proof of Credibility (PoC) for detecting fake news and blocking its propagation in social networks. The functionality of the PoC protocol has been simulated on two datasets of newsworthy tweets collected from different news sources on Twitter. The results clarified a satisfying performance and efficiency of the proposed approach in detecting rumors and blocking its propagation.

Silivanxay Phetsouvanh

Anwitaman Datta for his continuous support during my Ph.D. studies. His patient guidance, encouragement, and immense knowledge are precious to me and beyond what words can express. I really appreciate having a

Gengxin Sun, Sheng Bin, Meng Jiang, Ning Cao · 8 authors

With the emergence and development of blockchain technology, a new type of social networks based on blockchain had emerged. In these social networks high quality content creators, filters and propagators can all be reasonably motivated. Due to the transparency and traceability brought by blockchain technology, the public opinion propagation in such social networks presents new characteristics and laws. Based on the theory of network propagation and blockchain, a new public opinion propagation model for this kind of social network based on blockchain technology is proposed in this paper. The model considers the effect of incentive mechanism produced by reasonably quantifying value contribution on the propagation of information in such social networks, and the income-risk matrix under different propagation behaviors is constructed. Furthermore, the transformation process and transfer probability among different states in the propagation model are defined on the basis of income-risk matrix. The model is helpful to break the bottleneck of network public opinion management by using blockchain technology. The propagation of false network public opinion can be contained, and a good ecological environment of network public opinion propagation would be realized.

Naoki Kobayakawa, Kenichi Yoshida

No abstract is available for this record.

Mariana Durcheva, Pavel Tsankov

We investigate similarities and differences between stock and cryptocurrency networks obtained from log-return and volatility time series. We constructed correlation and Fast Fourier Transform based graphs and minimum spanning trees from a set of 100 highly capitalized cryptocurrencies and 100 highly capitalized NASDAQ stocks over a time window of fixed length. Our analysis is based on comparison between both economies in terms of network properties. We also examined distributions of node degrees and edge weights. Our results show that cryptocurrencies and companies with high capitalization tend to correspond to central and densely connected nodes. Network topologies for both economies and node degree distributions are rather similar. Nevertheless, the crypto-economy is more correlated and more strongly linked to important nodes, unlike the graphs of NASDAQ stocks, where we observed clusters of nodes having small dissimilarities.

Célestin Coquidé, José Lages, Dima L. Shepelyansky

We construct the Google matrices of bitcoin transactions for all year quarters during the period of January 11, 2009 till April 10, 2013. During the last quarters the network size contains about 6 million users (nodes) with about 150 million transactions. From PageRank and CheiRank probabilities, analogous to trade import and export, we determine the dimensionless trade balance of each user and model the contagion propagation on the network assuming that a user goes bankrupt if its balance exceeds a certain dimensionless threshold $\kappa$. We find that the phase transition takes place for $\kappa 0.55$ almost all users remain safe. We find that even on a distance from the critical threshold $\kappa_c$ the top PageRank and CheiRank users, as a house of cards, rapidly drop to the bankruptcy. We attribute this effect to strong interconnections between these top users which we determine with the reduced Google matrix algorithm. This algorithm allows to establish efficiently the direct and indirect interactions between top PageRank users. We argue that this study models the contagion on real financial networks.

A.K.M. Najmul Islam, Matti Mäntymäki, Marja Turunen

This paper investigates the focal actors and their heterogeneity in blockchain splits. Disagreements in blockchain communities often lead to splits in the blockchain and the community. For example, disagreements within the Bitcoin community on increasing the block size led to the blockchain split and creation of Bitcoin Cash. We build on actor-network theory to investigate blockchain split as a translation process, and employ case study methodology to examine Bitcoin splits. We identify several human actors, such as miners, developers, merchants, and investors, as well as non-human actors including ideologies, exchanges and computer programs involved in Bitcoin splits. Our results show that actor heterogeneity, that is, the complex constellation of diverse actors, plays a key role in blockchain splits. We further describe how the human and non-human actors’ fluid moves into micro and macro positions in the network affect the development of the split. We also discuss the role of these actors and their engagement in forming micro and macro agencies in blockchain splits. Our study adds to the understanding of actor behavior and network dynamics in decentralized information systems such as blockchain and open source software.

Silvia Bartolucci, Fabio Caccioli, Pierpaolo Vivo

The Lightning Network is a so-called second-layer technology built on top of the Bitcoin blockchain to provide "off-chain" fast payment channels between users, which means that not all transactions are settled and stored on the main blockchain. In this paper, we model the emergence of the Lightning Network as a (bond) percolation process and we explore how the distributional properties of the volume and size of transactions per user may impact its feasibility. The agents are all able to reciprocally transfer Bitcoins using the main blockchain and also - if economically convenient - to open a channel on the Lightning Network and transact "off chain". We base our approach on fitness-dependent network models: as in real life, a Lightning channel is opened with a probability that depends on the "fitness" of the concurring nodes, which in turn depends on wealth and volume of transactions. The emergence of a connected component is studied numerically and analytically as a function of the parameters, and the phase transition separating regions in the phase space where the Lightning Network is sustainable or not is elucidated. We characterize the phase diagram determining the minimal volume of transactions that would make the Lightning Network sustainable for a given level of fees or, alternatively, the maximal cost the Lightning ecosystem may impose for a given average volume of transactions. The model includes parameters that could be in principle estimated from publicly available data once the evolution of the Lighting Network will have reached a stationary operable state, and is fairly robust against different choices of the distributions of parameters and fitness kernels.

Andrii Bielinskyi, Vladimir Soloviev

This article demonstrates the possibility of constructing indicators of critical and crash phenomena in the volatile market of cryptocurrency. For this purpose, the methods of the theory of complex networks have been used. The possibility of constructing dynamic measures of network complexity behaving in a proper way during actual pre-crash periods has been shown. This fact is used to build predictors of crashes and critical events phenomena on the examples of all the patterns recorded in the time series of the key cryptocurrency Bitcoin, the effectiveness of the proposed indicators-precursors of these falls has been identified.

Melanie Swan

No abstract is available for this record.

Israa Alqassem, Iyad Rahwan, Davor Svetinović

Bitcoin is a cryptocurrency and a decentralized semi-anonymous peer-to-peer payment system in which the transactions are verified by network nodes and recorded in a public massively replicated ledger called the blockchain. Bitcoin is currently considered as one of the most disruptive technologies. Bitcoin represents a paradox of opposing forces. On one hand, it is fundamentally social, allowing people to transact in a peer-to-peer manner to create and exchange value. On the other hand, Bitcoin's core design philosophy and user base contain strong anti-social elements and constraints, emphasizing anonymity, privacy, and subversion of traditional centralized financial systems. We believe that the success of Bitcoin, and the financial ecosystem built around it, will likely rely on achieving an optimal balance between these social and anti-social forces. To elucidate the role of these forces, we analyze the evolution of the entire Bitcoin transaction graph from its inception, and quantify the evolution of its key structural properties. We observe that despite its different nature, the Bitcoin transaction graph exhibits many universal dynamics typical of social networks. However, we also find that Bitcoin deviates in important ways due to anonymity-seeking behavioral patterns of its users. As a result, the network exhibits a two-orders-of-magnitude larger diameter, sparse treelike communities, and an overwhelming majority of transitional or intermediate accounts with incoming and outgoing edges but zero cumulative balances. These results illuminate the evolutionary dynamics of the most popular cryptocurrency, and provide us with initial understanding of social networks rooted in and driven by anti-social constraints.

Marwane El Alaoui, Elie Bouri, David Roubaud

No abstract is available for this record.

Damiano Di Francesco Maesa, Andrea Marino, Laura Ricci

No abstract is available for this record.