Blockchain is a related FinTech asset but it is not the same technology. Basically, Blockchain is a decentralized and distributed digital ledger used to record Bitcoin transactions. The goal of this work is to employ multi-scale analysis to examine self-similarity in EDC Blockchain digital asset. Specifically, market technical data are examined; namely, open, high, low, and close. The resulting generalized Hurst exponent (GHE) estimates revealed that all Blockchain technical indicators exhibit multi-scale dynamics. In addition, short and long dynamics are different. It is concluded that market technical indicators associated with Blockchain provide valuable information for traders.
Natkamon Tovanich, Nicolas Soulié, Nicolas Heulot, Petra Isenberg
We demonstrate an interactive visualization tool to analyze Bitcoin mining pools. The tool allows analysts to see the evolution of mining pools distribution over time and relationships with external variables, i.e., Bitcoin statistics and news headlines. Moreover, we also display information about pool hopping among mining pools to help understand the internal dynamics of miners.
In this paper, we conduct a complex-network analysis of the Bitcoin network. In particular, we design a new sampling method namely random walk with flying-back (RWFB) to conduct effective data sampling. We then conduct a comprehensive analysis of the Bitcoin network in terms of the degree distribution, clustering coefficient, the shortest path length, the assortativity, and the rich-club coefficient. There are several important observations from the Bitcoin network, such as small- world phenomenon and non-rich-club effect. This work brings up an in-depth understanding of the current Bitcoin blockchain network and offers implications for future directions in malicious activity and fraud detection in cryptocurrency blockchain networks.
Xi He, Ketai He, Shenwen Lin, Jinglin Yang · 5 authors
Abstract Single heuristic method and incomplete heuristic conditions were difficult to cluster a large number of addresses comprehensively and accurately. Therefore, this paper analysed the associations between Bitcoin transactions and addresses and used six heuristic conditions to cluster addresses and entities. We proposed an improved change address detection algorithm and compared it with the original change address algorithm to prove the effectiveness of the improved algorithm. By adding conditional constraints, the identified change address was more accurate, and the convergence speed of the algorithm was accelerated. Our work presented the pseudo‐anonymity mechanism of the Bitcoin system, which could be used by the law enforcement agencies to track and crack down illegal transactions.
Lin Zhao, Sourav Sen Gupta, Arijit Khan, Robby Luo
With over 42 billion USD market capitalization (October 2020), Ethereum is the largest public blockchain that supports smart contracts. Recent works have modeled transactions, tokens, and other interactions in the Ethereum blockchain as static graphs to provide new observations and insights by conducting relevant graph analysis. Surprisingly, there is much less study on the evolution and temporal properties of these networks. In this paper, we investigate the evolutionary nature of Ethereum interaction networks from a temporal graphs perspective. We study the growth rate and model of four Ethereum blockchain networks, active lifespan and update rate of high-degree vertices. We detect anomalies based on temporal changes in global network properties, and forecast the survival of network communities in succeeding months leveraging on the relevant graph features and machine learning models.
Christoph Kinkeldey, Jean‐Daniel Fekete, Tanja Blascheck, Petra Isenberg
We present BitConduite, a visual analytics approach for explorative analysis of financial activity within the Bitcoin network, offering a view on transactions aggregated by entities, i.e., by individuals, companies, or other groups actively using Bitcoin. BitConduite makes Bitcoin data accessible to nontechnical experts through a guided workflow around entities analyzed according to several activity metrics. Analyses can be conducted at different scales, from large groups of entities down to single entities. BitConduite also enables analysts to cluster entities to identify groups of similar activities as well as to explore characteristics and temporal patterns of transactions. To assess the value of our approach, we collected feedback from domain experts.
We review probabilistic models known as majority dynamics (also known as threshold Voter Models) and discuss their possible applications for achieving consensus in cryptocurrency systems. In particular, we show that using this approach straightforwardly for practical consensus in Byzantine setting can be problematic and requires extensive further research. We then discuss the FPC consensus protocol which circumvents the problems mentioned above by using external randomness.
In recent years, Blockchain has become a disruptivetechnology to protect the integrity of information, especially inopen and collaborative information systems. Its main advantageis the possibility to reach consensus on the new data blocks tobe added to the chain, even with anonymous actors. The mostcommon consensus mechanism is Proof of Work, but it has beenproven to be very inefficient in terms of energy spent by themembers of the blockchain. In the literature there are many othertechniques that pretend to become the new popular mechanism.However, the number of this techniques is growing too fast toreally be able to differentiate among all the options. In this work,a new characterization of consensus algorithm is proposed, thatcan be used to find families of mechanism using cluster-basedclassification. Using the Ward Method and Spearmans RankCorrelation analysis, new clusters of consensus mechanisms wereidentified. The results describe the behavioral patterns not seenbefore in the literature. In addition, some open problems ofcurrent consensus algorithms are discussed.
Unlike conventional cryptocurrencies, Islamic ones are new technologies backed by tangible assets and are characterised by their fundamental values. After the COVID-19 outbreak, cryptocurrency responses have shown different behaviour to stock market reactions. However, there is a lack of studies on the efficiency of Islamic and green cryptocurrencies during the pandemic. This paper attempts to analyse the behaviour of three typical families of cryptocurrencies (conventional, Islamic, and green) extracted according to their availability in daily frequencies during COVID-19. For this purpose, their efficiency levels are studied before and after the outbreak by employing multifractal detrended fluctuation analysis (MFDFA) to make the best predictions and strategies. The inefficiency of the cryptocurrencies is assessed through a magnitude of long-memory (MLM) efficiency index, and the impact of COVID-19 on their efficiency is evaluated. The primary results show that HelloGold was the most efficient market before the COVID-19 outbreak and that subsequently Ethereum has been the most efficient. In addition, the findings reveal that the cryptocurrency reactions are not similar and show more resilience in the Ethereum and Litecoin markets than in other cryptocurrency markets. The main contribution of this study is the evaluation of the impact of COVID-19 on the various classes of crypto money. This work has practical implications, as it provides new insights into trading opportunities and market reactions. Moreover, he work has theoretical implications based on its evaluation of three distinct models from different doctrine viewpoints.
Cüneyt Gürcan Akçora, Yulia R. Gel, Murat Kantarcıoğlu
Abstract Blockchain is an emerging technology that has enabled many applications, from cryptocurrencies to digital asset management and supply chains. Due to this surge of popularity, analyzing the data stored on blockchains poses a new critical challenge in data science. To assist data scientists in various analytic tasks for a blockchain, in this tutorial, we provide a systematic and comprehensive overview of the fundamental elements of blockchain network models. We discuss how we can abstract blockchain data as various types of networks and further use such associated network abstractions to reap important insights on blockchains' structure, organization, and functionality. This article is categorized under: Technologies > Data Preprocessing Application Areas > Business and Industry Fundamental Concepts of Data and Knowledge > Data Concepts Fundamental Concepts of Data and Knowledge > Knowledge Representation
Cüneyt Gürcan Akçora, Murat Kantarcıoğlu, Yulia R. Gel
Blockchain is an emerging technology that has enabled many applications, from\ncryptocurrencies to digital asset management and supply chains. Due to this\nsurge of popularity, analyzing the data stored on blockchains poses a new\ncritical challenge in data science.\n To assist data scientists in various analytic tasks on a blockchain, in this\ntutorial, we provide a systematic and comprehensive overview of the fundamental\nelements of blockchain network models. We discuss how we can abstract\nblockchain data as various types of networks and further use such associated\nnetwork abstractions to reap important insights on blockchains' structure,\norganization, and functionality.\n
CryptoKitties was the first widely recognized blockchain game. Players could own, breed, and trade kitties, which are the only prop in the game. The game gained explosive growth upon its release but quickly collapsed in a short time. This study analyzes its entire player activity history for the first time in literature and tries to find the reasons for the rise and fall of this first blockchain game mania. First, we extracted the five million transaction records among 100 thousand addresses involved in CryptoKitties in the past three years. Based on the numbers of addresses involved in the game each day, we divide the game progress into four stages: the primer, the rise, the fall, and the serenity. We construct a temporal kitty ownership transfer network and analyze the varying network parameters in the four stages. We find that a large number of players poured in during the 10th and 18th days since the game release and quickly exited in the following month. Since then, a few big players have gradually dominated the game, concentrating the game resources. Through further analysis, we find that the main reason for the rapid increase in the game popularity was the increase of public attention by media outlets, while the reasons for the rapid decline in the game popularity include the oversupply of kitties, the decreasing of player income, a widening gap between the rich and poor players, and the limitations of blockchain systems. Based on these observations, we advise on the further blockchain game design: (1) to finely control the production of props and avoid an oversupply, (2) to balance the gaming cost and revenue and protect the enjoyment of players, (3) to narrow down the gap between rich and poor and create an equal gaming community, (4) to consider the limitations of blockchain systems in their game designs.
Dániel Kondor, Nikola Bulatovic, József Stéger, István Csabai · 5 authors
Bitcoin and Ethereum transactions present some of the largest real-world complex networks that are publicly available for study, including a detailed picture of their time evolution. As such, they have received a considerable amount of attention from the network science community along with analyses from economic and cryptographic perspectives. Among these studies, in an analysis on the early instance of the Bitcoin network, we have shown the clear presence of the preferential attachment, or the “rich-get-richer” phenomenon. Now, we revisit this question, using a recent version of the Bitcoin network that has grown almost 100-fold since our original analysis. Furthermore, we additionally carry out a comparison with Ethereum, the second most important cryptocurrency. Our results show that preferential attachment continues to be a key factor in the evolution of both the Bitcoin and Ethereum transactoin networks. To facilitate further analysis, we publish a recent version of both transaction networks, and an efficient software implementation that is able to evaluate linking statistics necessary for learn about preferential attachment on networks with several hundred million edges.
Dániel Kondor, Nikola Bulatović, József Stéger, István Csabai · 5 authors
Bitcoin and Ethereum transactions present one of the largest real-world\ncomplex networks that are publicly available for study, including a detailed\npicture of their time evolution. As such, they have received a considerable\namount of attention from the network science community, beside analysis from an\neconomic or cryptography perspective. Among these studies, in an analysis on\nthe early instance of the Bitcoin network, we have shown the clear presence of\nthe preferential attachment, or "rich-get-richer" phenomenon. Now, we revisit\nthis question, using a recent version of the Bitcoin network that has grown\nalmost 100-fold since our original analysis. Furthermore, we additionally carry\nout a comparison with Ethereum, the second most important cryptocurrency. Our\nresults show that preferential attachment continues to be a key factor in the\nevolution of both the Bitcoin and Ethereum transactoin networks. To facilitate\nfurther analysis, we publish a recent version of both transaction networks, and\nan efficient software implementation that is able to evaluate linking\nstatistics necessary for learn about preferential attachment on networks with\nseveral hundred million edges.\n
Abstract In this study, we characterized the dynamics and analyzed the degree of synchronization of the time series of daily closing prices and volumes in US$ of three cryptocurrencies, Bitcoin, Ethereum, and Litecoin, over the period September 1,2015–March 31, 2020. Time series were first mapped into a complex network by the horizontal visibility algorithm in order to revel the structure of their temporal characters and dynamics. Then, the synchrony of the time series was investigated to determine the possibility that the cryptocurrencies under study co-bubble simultaneously. Findings reveal similar complex structures for the three virtual currencies in terms of number and internal composition of communities. To the aim of our analysis, such result proves that price and volume dynamics of the cryptocurrencies were characterized by cyclical patterns of similar wavelength and amplitude over the time period considered. Yet, the value of the slope parameter associated with the exponential distributions fitted to the data suggests a higher stability and predictability for Bitcoin and Litecoin than for Ethereum. The study of synchrony between the time series investigated displayed a different degree of synchronization between the three cryptocurrencies before and after a collapse event. These results could be of interest for investors who might prefer to switch from one cryptocurrency to another to exploit the potential opportunities of profit generated by the dynamics of price and volumes in the market of virtual currencies.
Alex Auvolat, Yérom-David Bromberg, Davide Frey, François Taı̈ani
Recent works have proposed new Byzantine consensus algorithms for blockchains based on epidemics, a design which enables highly scalable performance at a low cost. These methods however critically depend on a secure random peer sampling service: a service that provides a stream of random network nodes where no attacking entity can become over-represented. To ensure this security property, current epidemic platforms use a Proof-of-Stake system to select peer samples. However such a system limits the openness of the system as only nodes with significant stake can participate in the consensus, leading to an oligopoly situation. Moreover, this design introduces a complex interdependency between the consensus algorithm and the cryptocurrency built upon it. In this paper, we propose a radically different security design for the peer sampling service, based on the distribution of IP addresses to prevent Sybil attacks. We propose a new algorithm, $\scriptstyle{BASALT}$, that implements our design using a stubborn chaotic search to counter attackers' attempts at becoming over-represented. We show in theory and using Monte Carlo simulations that $\scriptstyle{BASALT}$ provides samples which are extremely close to the optimal distribution even in adversarial scenarios such as tentative Eclipse attacks. Live experiments on a production cryptocurrency platform confirm that the samples obtained using $\scriptstyle{BASALT}$ are equitably distributed amongst nodes, allowing for a system which is both open and where no single entity can gain excessive power.
Some Internet of Things (IoT) platforms use blockchain to transport data. The value proposition of IoT is the connection to the Internet of a myriad of devices that provide and exchange data to improve people’s lives and add value to industries. The blockchain technology transfers data and value in an immutable and decentralised fashion. Security, composed of both non-intentional and intentional risk management, is a fundamental design requirement for both IoT and blockchain. We study how blockchain answers some of the IoT security requirements with a focus on intentional risk. The review of a sample of security incidents impacting public blockchains confirm that identity and access management (IAM) is a key security requirement to build resilience against intentional risk. This fact is also applicable to IoT solutions built on a blockchain. We compare the two IoT platforms based on public permissionless distributed ledgers with the highest market capitalisation: IOTA, run on an alternative to a blockchain, which is a directed acyclic graph (DAG); and IoTeX, its contender, built on a blockchain. Our objective is to discover how we can create IAM resilience against intentional risk in these IoT platforms. For that, we turn to complex network theory: a tool to describe and compare systems with many participants. We conclude that IoTeX and possibly IOTA transaction networks are scale-free. As both platforms are vulnerable to attacks, they require resilience against intentional risk. In the case of IoTeX, DIoTA provides a resilient IAM solution. Furthermore, we suggest that resilience against intentional risk requires an IAM concept that transcends a single blockchain. Only with the interplay of edge and global ledgers can we obtain data integrity in a multi-vendor and multi-purpose IoT network.
Twelve years after Satoshi Nakamoto published the paper describing the functioning mechanism and principals of cryptocurrency that maintains secure and anonymous digital transactions beyond any banks, cryptocurrencies have become a multi-billion-dollar industry comprising millions of investors, miners, developers and profiteers. However, the actual price determinants and ways to forecast future price changes remain an open question yet to discover the answer for. This study attempts to figure out whether media hype exerts that much influence upon cryptocurrencies price movements and whether it can be used as the basis for future movements prediction. Two cryptocurrencies, Bitcoin and Tezos, and 7 mass and social media factors for each of them were considered on daily basis from 08-01-2018 to 10-31-2020. To explore the interdependence between media drivers and cryptocurrencies' prices in short, medium and long timespan, this study deploys wavelet coherence approach. There was found, that price changes turn to be the supreme prior to hype, even though the growing ado may push the prices even higher. Thus, hype is failing to prove itself as a reliable cryptocurrency price predictor. Crypto investors, though, should anyways take the news background into account while building trading strategies,...
Nicolò Vallarano, Matteo Bruno, Emiliano Marchese, Giuseppe Di Trapani · 8 authors
Exponential Random Graph Models (ERGMs) have gained increasing popularity over the years. Rooted into statistical physics, the ERGMs framework has been successfully employed for reconstructing networks, detecting statistically significant patterns in graphs, counting networked configurations with given properties. From a technical point of view, the ERGMs workflow is defined by two subsequent optimization steps: the first one concerns the maximization of Shannon entropy and leads to identify the functional form of the ensemble probability distribution that is maximally non-committal with respect to the missing information; the second one concerns the maximization of the likelihood function induced by this probability distribution and leads to its numerical determination. This second step translates into the resolution of a system of $O(N)$ non-linear, coupled equations (with $N$ being the total number of nodes of the network under analysis), a problem that is affected by three main issues, i.e. accuracy, speed and scalability. The present paper aims at addressing these problems by comparing the performance of three algorithms (i.e. Newton's method, a quasi-Newton method and a recently-proposed fixed-point recipe) in solving several ERGMs, defined by binary and weighted constraints in both a directed and an undirected fashion. While Newton's method performs best for relatively little networks, the fixed-point recipe is to be preferred when large configurations are considered, as it ensures convergence to the solution within seconds for networks with hundreds of thousands of nodes (e.g. the Internet, Bitcoin). We attach to the paper a Python code implementing the three aforementioned algorithms on all the ERGMs considered in the present work.
Smart contract risk can be defined as a financial risk of loss due to cyber attacks on or contagious failures of smart contracts. Its quantification is of paramount importance to technology platform providers as well as companies and individuals when considering the deployment of this new technology. That is why, as our primary contribution, we propose a structural framework of aggregate loss distribution for smart contract risk under the assumption of a tree-stars graph topology representing the network of interactions among smart contracts and their users. Up to our knowledge, there exist no theoretical frameworks or models of an aggregate loss distribution for smart contracts in this setting. To achieve our goal, we contextualize the problem in the probabilistic graph-theoretical framework using bond percolation models. We assume that the smart contract network topology is represented by a random tree graph of finite size, and that each smart contract is the center of a {random} star graph whose leaves represent the users of the smart contract. We allow for heterogeneous loss topology superimposed on this smart contract and user topology and provide analytical results and instructive numerical examples.
In this work, we consider community detection in blockchain networks. We specifically take the Bitcoin network and Ethereum network as two examples, where community detection serves in different ways. For the Bitcoin network, we modify the traditional community detection method and apply it to the transaction social network to cluster users with similar characteristics. For the Ethereum network, on the other hand, we define a bipartite social graph based on the smart contract transactions. A novel community detection algorithm which is designed for low-rank signals on graph can help find users' communities based on user-token subscription. Based on these results, two strategies are devised to deliver on-chain advertisements to those users in the same community. We implement the proposed algorithms on real data. By adopting the modified clustering algorithm, the community results in the Bitcoin network is basically consistent with the ground-truth of betting site community which has been announced to the public. At the meanwhile, we run the proposed strategy on real Ethereum data, visualize the results and implement an advertisement delivery on the Ropsten test net.
Online social networks (OSN) are becoming more important in people's daily life, however, all popular OSNs are centralized, and this raises a series of security, privacy and management issues. A decentralized architecture based on blockchain technology provides the ability to solve above issues. In this paper, an OSN service is developed based on blockchain technology in order to make it operate decentralized. Large volume of data normally required low-security requirements can be stored in Interplanetary Filesystem (IPFS) to make data decentralized. A decentralized autonomous organization is developed for user autonomy, users can self-manage the OSN in a democratic way.