M S Bhargavi, Sushmitha M Katti, M Shilpa, Vaishnavi P Kulkarni · 5 authors
Ethereum is a blockchain based development platform for users to build and deploy decentralized applications and smart contracts. Transactions substituting money over Ethereum space are carried out using Ethereum's cryptocurrency `Ether'. Though the decentralization and public ledger recording of the transactions proves its limpidity, the anonymity of the users, hiding their true identity behind the addresses echoes the need for discerning the behavioural traits in transactions. The appalling nature of Ethereum transactions to have both, security and threat at a comparable level, demands analytics for inferring traits for better perspectives and insights. This research work focuses on characteristic analysis of Ethereum transaction space for inferring behavioural traits in supervised and unsupervised context. In an unsupervised environment, raw transaction data is extradited to a tabular transaction structure by selecting appropriate features. The data is further clustered and validated to form coherent groupings of similar patterns. The clusters are characteristically analyzed based on the features through Radar plots for inferring behavioural traits. In supervised context, labelled transactions are represented using histograms and feature-based characteristic analysis is performed to infer traits. Such analytics lay foundations for future deeper analysis that can be used to discover trends and patterns to understand the transaction network, enhancement of trading strategies, identification of bot activities, anomaly detection and several others.
There is abundantly documented scientific evidence that the financial transactions that have grown rapidly recently, in conjuction with the interest of the public, were due to the sharp rise in the price of Bitcoin in December 2017. As a consequence, a freshly emerging dataset in the research community has emerged. Therefore, the aim of the present investigation was to examine the analyses of data in this newly emerging dataset in the research community. In order to achieve the extraction of data, their conversion to network and finally their fragmentation, the studied variables were analyzed by using two parts of analysis, namely, statistical network analyses and economic activity analyses. Network statistical analyses was employed aiming to analyze, in a holistic approach, the complex systems of modern times which are represented as networks, as it is impossible to analyze them partially, in order to avoid incorrect conclusions. Additionally, the analyses of economic activity, which is related to indicators from the stock market and the economics of science, was used, after it had been transferred and matched with the economic model represented by Bitcoin. The results distinguished the extent of the data generated by the statistical analyses of the networks and the analyses of economic activity. With respect to data presented, we established that the daily transaction networks were scale free networks which were not evolving like ER random networks and they were not defined as the small world. Also, it was demonstrated that daily transaction networks cannot be reproduced in a random way like ER random networks. Furthermore, the opportunities and problems encountered in conducting the present research were briefly presented.
Blockchain is a distributed ledger technology for recording transactions. To guarantee the immutability of the blocks, miners need a lot of computational resources for generating blocks (mining). Since mining is conducted distributedly by many miners, chain forks can occur in the blockchain. In this paper, we investigate the effects of miners’ locations, the size of the mining pool (MP), and the network structure on the selection rate of blocks generated by the MP when chain forks occur through simulations.
Juliana Zanelatto Gavião Mascarenhas, Artur Ziviani, Klaus Wehmuth, Alex Borges Vieira
Abstract Distributed blockchain-based consensus platforms have witnessed steady growth in recent years. In special, cryptocurrency is one of the main applications of the blockchain technology. Despite the recent interest in blockchain, we still lack in-depth analysis of systems that use such a technology. In fact, most of the existing works focus on Bitcoin. Moreover, blockchain-based cryptocurrency systems are highly dynamic. Their internal mechanisms and consensus algorithms evolve over time. Users also change their interests in a given platform, which in turn, reflect their behaviour. In this article, we model the Ethereum-based cryptocurrency transaction network, a more recent blockchain platform that is gaining a significant share in the cryptocurrency market. We model the transactions of Ethereum as a complex system, representing this complex system as a time-varying graph. Our model and the analysis we conduct rely on a 3-year dataset of Ethereum-based cryptocurrency transactions, comprising more than 38 million users (i.e. unique wallet addresses) and almost 300 million transactions. We analyse the evolution of users and transactions over time. Our study also highlights the centralization tendency of the transaction network on both user and time aspects. Finally, we also analyse the formation of communities and the evolution of connected components considering the dynamics of the Ethereum-based cryptocurrency transaction network.
Rubaiyat Islam, Yoshi Fujiwara, Shinya Kawata, Hiwon Yoon
In this study, we analyzed bitcoin blockchain data for the period between 2013 and 2018. We constructed daily networks and analyzed the network properties of the bitcoin users and bitcoin flow attributed as edge flow circulated between users to focus on weekdays and weekends activities. In the real world, businesses take time off, particularly on weekends. This is no different in the crypto-asset world, which, theoretically, is operable 24/7. We also performed a threshold analysis of the flow of bitcoin to identify the big wallets and to compare it with the identity of real-world crypto-exchange companies, in particular, their weekly patterns. Finally, we propose a methodology to identify the financial institution in the bitcoin blockchain on the basis of fulfilling some key criteria. The criteria are having high frequency, appearing persistently on daily big trades and showing a distinct weekly pattern of total average network flow.
Off-chain transaction channels represent one of the leading techniques to scale the transaction throughput in cryptocurrencies such as Bitcoin. They allow multiple agents to route payments through one another. So far, the topology and construction of payment networks has not been explored much. Participants are expected to minimize costs that are due to the allocation of liquidity as well as blockchain record fees. In this paper we study the optimization of maintenance costs of such networks. We present for the first time, a closed model for symmetric off-chain channels, and provide efficient algorithms for constructing minimal cost spanning-tree networks under this model. We prove that for any network demands, a simple hub topology provides a 2-approximation to the minimal maintenance cost showing that spanning trees in general are efficient. We also show an unbounded price of anarchy in a greedy game between the transactors, when each player wishes to minimize his costs by changing the network's structure. Finally, we simulate and compare the costs of payment networks with scale free demand topologies.
Abstract Directed Graph based models of a blockchain that capture accounts as nodes and transactions as edges, evolve over time. This temporal nature of a blockchain model enables us to understand the behavior (malicious or benign) of the accounts. Predictive classification of accounts as malicious or benign could help users of the permissionless blockchain platforms to operate in a secure manner. Motivated by this, we introduce temporal features such as burst and attractiveness on top of several already used graph properties such as the node degree and clustering coefficient. Using identified features, we train various Machine Learning (ML) models and identify the algorithm that performs the best in detecting malicious accounts. We then study the behavior of the accounts over different temporal granularities of the dataset before assigning them malicious tags. For the Ethereum blockchain, we identify that for the entire dataset—the ExtraTreesClassifier performs the best among supervised ML algorithms. On the other hand, using cosine similarity on top of the results provided by unsupervised ML algorithms such as K-Means on the entire dataset, we were able to detect 554 more suspicious accounts. Further, using behavior change analysis for accounts, we identify 814 unique suspicious accounts across different temporal granularities.
The temporal nature of modeling accounts as nodes and transactions as\ndirected edges in a directed graph -- for a blockchain, enables us to\nunderstand the behavior (malicious or benign) of the accounts. Predictive\nclassification of accounts as malicious or benign could help users of the\npermissionless blockchain platforms to operate in a secure manner. Motivated by\nthis, we introduce temporal features such as burst and attractiveness on top of\nseveral already used graph properties such as the node degree and clustering\ncoefficient. Using identified features, we train various Machine Learning (ML)\nalgorithms and identify the algorithm that performs the best in detecting which\naccounts are malicious. We then study the behavior of the accounts over\ndifferent temporal granularities of the dataset before assigning them malicious\ntags. For Ethereum blockchain, we identify that for the entire dataset - the\nExtraTreesClassifier performs the best among supervised ML algorithms. On the\nother hand, using cosine similarity on top of the results provided by\nunsupervised ML algorithms such as K-Means on the entire dataset, we were able\nto detect 554 more suspicious accounts. Further, using behavior change analysis\nfor accounts, we identify 814 unique suspicious accounts across different\ntemporal granularities.\n
The work here presented moves from the acknowledgment that, even if blockchain technology has been around for more than ten years, the knowledge about its economic and business implications is fragmented and heterogeneous. In the first place, it is analyzed the shift from economics to tokenomics and the central role of the token within blockchain-based ecosystems. Subsequently, a generalized definition of the token is proposed. Diving into the requirements for a comprehensive description of tokens, that takes into account their wide variety, a comparative assessment of token classification frameworks available in the literature is performed. This analysis is leveraged to propose a new and comprehensive token classification framework, based on a morphological analysis representation. The proposed framework will be further refined with an empirical and iterative approach in future works.
Ethereum is one of the most popular blockchain systems that supports more than half a million transactions every day. Whereas it remains mysterious what the transaction pattern is and how it evolves over time. In this paper, we study the evolutionary behavior of Ethereum transactions from a temporal graph point of view. It shows that there is no evidence that changes in average triplet closure duration is related to prices. We observe the macroscopic and microscopic burstiness of Ethereum transactions. We analyze the Gini indexes of the transaction graphs and the user wealth in which Ethereum is found to be very unfair since the very beginning, in a sense, “the rich is already very rich”.
Abstract The explosive growth of data in the network has brought huge burdens and challenges to traditional centralized cloud computing data processing. To solve this problem, edge computing technology came into being. Because the edge is closer to the user, processing part of the data at the edge can also bring a faster response to the user and improve their experience. However, the existing edge computing platforms have problems such as data storage security and multiparty data mutual trust. Blockchain technology has become an important means to solve the above data storage and sharing problems due to its excellent characteristics. The core of blockchain technology is consensus, and its speed and security will directly affect the efficiency and stability of the blockchain system. Therefore, this study uses the consensus mechanism as an entry point to reduce the resource consumption of the edge computing blockchain system and improve its security. In order to reduce the resource consumption of traditional consensus algorithms, improve their adaptability in the edge computing environment, and solve the security problem caused by the concentration of node rights, a prestige‐based edge computing blockchain security consensus model (ECBCM) is proposed. ECBCM is a general model based on prestige rewards and penalties. It also introduces a node replacement mechanism to ensure the fault tolerance of the consensus process. According to the results of multiple sets of performance comparison experiments and security verification experiments after embedding the existing consensus algorithm, the validity of the consensus model is confirmed.
We consider a particular instance of user interactions in the Bitcoin network, that of interactions among wallet addresses belonging to scammers. Aggregation of multiple inputs and change addresses are common heuristics used to establish relationships among addresses and analyze transaction amounts in the Bitcoin network. We propose a flow centric approach that complements such heuristics, by studying the branching, merger and propagation of Bitcoin flows. We study a recent sextortion campaign by exploring the ego network of known offending wallet addresses. We compare and combine different existing and new heuristics, which allows us to identify (1) Bitcoin addresses of interest (including possible recurrent go-to addresses for the scammers) and (2) relevant Bitcoin flows, from scam Bitcoin addresses to a Binance exchange and to other other scam addresses, that suggest connections among prima facie disparate waves of similar scams.
As the pioneer of blockchain technology, Bitcoin is the most popular cryptocurrency to date. Given its dramatic price spikes (and crashes) along with the never-ending news from SEC regulations to security breaches, there seems to be a lack of understanding about the dynamics of cryptocurrencies. These dynamics are believed to be affected by various political, security, financial, and regulatory events. In this paper, we present an efficient framework for holistic analysis of cryptocurrency fluctuations by introducing the Impact-Score metric to distinguish event-induced changes from normal variations. We have applied our framework to 16 major worldwide events and the Bitcoin blockchain network (defined as Bitcoin transaction and users, blockchain data, and memory pool data) from 2016-2018. The results show that a majority of the events are correlated with substantial network changes. We observed roughly generalizable correlations between event types (e.g. financial events) and sub-structures of the Bitcoin blockchain network. Subgroups of these events have strongly consistent temporal impacts on specific facets (e.g. activity or fees) of the Bitcoin ecosystem. Furthermore, we demonstrate the robustness of our process by correlating a majority of spikes in network/subnetwork change with major events.
Cryptocurrencies have become a prominent investment tool recently with increasing interest in them and their relationships with stock and foreign exchange markets. We analyze here the cross-correlations of price changes of different cryptocurrencies using Random Matrix Theory and extract community structures by constructing minimum spanning trees, finding their eigenvalues contrast sharply with universal predictions of Random Matrix Theory. We reveal distinct transient community structures among different groupings of cryptocurrencies. By studying the cross-correlation dynamics of sub-communities we find evidence of collective behaviour. Furthermore, we compare eigenvalue changes and find prominent groupings following a community trend, useful for creating cryptocurrency portfolios.
Blockchain is a public distributed ledger, which has the characteristics of decentralization and anonymization, which leads to the frequent occurrence of money laundering and theft. Taking Bitcoin as an example, traders can have multiple addresses, and these addresses have nothing to do with their identities in real life, their identities are difficult to identify, and it is difficult to track the flow of transaction funds on the blockchain. This paper proposes a transaction tracking system that can effectively and accurately track the source and destination of a certain amount of funds on the blockchain, which is superior to existing Bitcoin transaction tracking methods and has a substantial reference value.
Nicolò Vallarano, Claudio J. Tessone, Tiziano Squartini
Cryptocurrencies are distributed systems that allow exchanges of native (and non-) tokens between participants. The availability of the complete historical bookkeeping opens up an unprecedented possibility: that of understanding the evolution of a cryptocurrency's network structure while gaining useful insights into the relationships between users' behavior and cryptocurrency pricing in exchange markets. In this article we review some recent results concerning the structural properties of the Bitcoin Transaction Networks , a generic name referring to a set of three different constructs: the Bitcoin Address Network , the Bitcoin User Network , and the Bitcoin Lightning Network . The picture that emerges is of a system growing over time, which becomes increasingly sparse and whose mesoscopic structural organization is characterized by the presence of an increasingly significant core-periphery structure. Such a peculiar topology is accompanied by a highly uneven distribution of bitcoins, a result suggesting that Bitcoin is becoming an increasingly centralized system at different levels.
The usage of cryptocurrencies, together with that of financial automated consultancy, is widely spreading in the last few years. However, automated consultancy services are not yet exploiting the potentiality of this nascent market, which represents a class of innovative financial products that can be proposed by robo-advisors. For this reason, we propose a novel approach to build efficient portfolio allocation strategies involving volatile financial instruments, such as cryptocurrencies. In other words, we develop an extension of the traditional Markowitz model which combines Random Matrix Theory and network measures, in order to achieve portfolio weights enhancing portfolios' risk-return profiles. The results show that overall our model overperforms several competing alternatives, maintaining a relatively low level of risk.
Derick Quintino, Jéssica Suárez Campoli, Heloísa Lee Burnquist, Paulo Ferreira
Bitcoin’s evolution has attracted the attention of investors and researchers looking for a better understanding of the efficiency of cryptocurrency markets, considering their prices and volatility. The purpose of this paper is to contribute to this understanding by studying the degree of persistence of the Bitcoin measured by the Hurst exponent, considering prices from the Brazilian market, and comparing with Bitcoin in USD as a benchmark. We applied Detrended Fluctuation Analysis (DFA), for the period from 9 April 2017 to 30 June 2018, using daily closing prices, with a total of 429 observations. We focused on two prices of Bitcoins resulting from negotiations made by two different Brazilian financial institutions: Foxbit and Mercado. The results indicate that Mercado and Foxbit returns tend to follow Bitcoin dynamics and all of them show persistent behavior, although the persistence in slightly higher for the Brazilian Bitcoin. However, this evidence does not necessarily mean opportunities for abnormal profits, as aspects such as liquidity or transaction costs could be impediments to this occurrence.
Xi Tong Lee, Arijit Khan, Sourav Sen Gupta, Yu Hann Ong · 5 authors
Blockchains are increasingly becoming popular due to the prevalence of cryptocurrencies and decentralized applications. Ethereum is a distributed public blockchain network that focuses on running code (smart contracts) for decentralized applications. More simply, it is a platform for sharing information in a global state that cannot be manipulated or changed. Ethereum blockchain introduces a novel ecosystem of human users and autonomous agents (smart contracts). In this network, we are interested in all possible interactions: user-to-user, user-to-contract, contract-to-user, and contract-to-contract. This requires us to construct interaction networks from the entire Ethereum blockchain data, where vertices are accounts (users, contracts) and arcs denote interactions. Our analyses on the networks reveal new insights by combining information from the four networks. We perform an in-depth study of these networks based on several graph properties consisting of both local and global properties, discuss their similarities and differences with social networks and the Web, draw interesting conclusions, and highlight important, future research directions.
Shahar Somin, Goren Gordon, Alex Pentland, Erez Shmueli · 5 authors
Following the birth of Bitcoin and the introduction of the Ethereum ERC20 protocol a decade ago, recent years have witnessed a growing number of cryptographic tokens that are being introduced by researchers, private sector companies and NGOs. The ubiquitous of such Blockchain based cryptocurrencies give birth to a new kind of rising economy, which presents great difficulties to modeling its dynamics using conventional semantic properties. Our work presents the analysis of the dynamical properties of the ERC20 protocol compliant crypto-coins' trading data using a network theory prism. We examine the dynamics of ERC20 based networks over time by analyzing a meta-parameter of the network, the power of its degree distribution. Our analysis demonstrates that this parameter can be modeled as an under-damped harmonic oscillator over time, enabling a year forward of network parameters predictions.