Abstract Purpose In this paper, we define the concept of user spectrum and adopt it to classify Ethereum users based on their behavior. Design/methodology/approach Given a time period, our approach associates each user with a spectrum showing the trend of some behavioral features obtained from a social network-based representation of Ethereum. Each class of users has its own spectrum, obtained by averaging the spectra of its users. In order to evaluate the similarity between the spectrum of a class and the one of a user, we propose a tailored similarity measure obtained by adapting to this context some general measures provided in the past. Finally, we test our approach on a dataset of Ethereum transactions. Findings We define a social network-based model to represent Ethereum. We also define a spectrum for a user and a class of users (i.e., token contract, exchange, bancor and uniswap), consisting of suitable multivariate time series. Furthermore, we propose an approach to classify new users. The core of this approach is a metric capable of measuring the similarity degree between the spectrum of a user and the one of a class of users. This metric is obtained by adapting the Eros distance (i.e., Extended Frobenius Norm) to this scenario. Originality/value This paper introduces the concept of spectrum of a user and a class of users, which is new for blockchains. Differently from past models, which represented user behavior by means of univariate time series, the user spectrum here proposed exploits multivariate time series. Moreover, this paper shows that the original Eros distance does not return satisfactory results when applied to user and class spectra, and proposes a modified version of it, tailored to the reference scenario, which reaches a very high accuracy. Finally, it adopts spectra and the modified Eros distance to classify Ethereum users based on their past behavior. Currently, no multi-class automatic classification approach tailored to Ethereum exists yet, albeit some single-class ones have been recently proposed. Therefore, the only way to classify users in Ethereum are online services (e.g., Etherscan), where users are classified after a request from them. However, the fraction of users thus classified is low. To address this issue, we present an automatic approach for a multi-class classification of Ethereum users based on their past behavior.
Time series forecasting is a big fuss or it can be said that it’s a trending topic that has many feasible implementations including stock prices prediction, weather prediction, trade design and capital allotment. Therefore, it contributes to a large number of applications in the controversial domain of blockchain. In the realm of blockchain, forecasting is necessary so as to predict the future prospects. Owing to its immutable and decentralised nature, blockchain facilitates the tracking of malicious activities via anomaly detection. In the recent past, machine learning is spreading its root in every domain and blockchain is not left unmarked by it. It enforces the system to learn from past experience, acknowledge the pattern and thus apply that knowledge in future. In this work, we measure the prophecy or predicting capability of the bitcoin time sequence. This work presents an extensive review of machine learning algorithms along with an implemented study of ARIMA model, LSTM model and XGBoost to predict the fluctuations on a monthly basis. Further, the paper introduces K-means clustering-based anomaly detection scheme that predicts anomalies on the basis of timestamps in Weighted price and Volume of bitcoin. It has been shown that prediction of the anomalies on the dataset yields favourable outcomes and surpasses the outcomes of forecasting in terms of accuracy. Finally, the paper concludes by highlighting several open research challenges in the field of study.
Understanding the semantic of a collection of texts is a challenging task. Topic models are probabilistic models that aims at extracting "topics" from a corpus of documents. This task is particularly difficult when the corpus is composed of short texts, such as posts on social networks. Following several previous research papers, we explore in this paper a set of collected tweets about bitcoin. In this work, we train three topic models and evaluate their output with several scores. We also propose a concrete application of the extracted topics.
Natkamon Tovanich, Nicolas Soulié, Nicolas Heulot, Petra Isenberg
We analyzed 23 mining pools and explore the mobility of miners throughout Bitcoin’s history. Mining pools have emerged as major players to ensure that the Bitcoin system stays secure, valid, and stable. Many questions remain open regarding how mining pools have evolved throughout Bitcoin’s history and when and why miners join or leave the pools. We investigated the reward payout flow of mining pools and characterized them based on payout irregularity and structural complexity. Based on our proposed algorithm, we identified miners and studied their mobility in the pools over time. Our analysis shows that Bitcoin mining is an industry that is sensitive to external events (e.g., market price and government policy). Over time, competition between pools involving reward schemes and pool fees motivated miners to migrate between pools (i.e., pool hopping and cross pooling). These factors converged toward optimal scheme and values, which made mining activities more stable.
Blockchain technology has been around for more than ten years, nevertheless, the knowledge about its economic and business implications is still fragmented and heterogeneous. The present article intends to tackle this issue with a twofold contribution. The first is an analysis of the shift from economics to tokenomics highlighting the central role played by tokens within blockchain-based ecosystems. The second is a framework for tokens design leveraging a morphological analysis deeply grounded in the literature. As blockchain becomes a mainstream phenomenon, the value of the work proposed lies in lowering the cognitive barriers and in clarifying the space of available options for private and public actors willing to leverage tokenization in their daily operations.
Klaus Grobys, Josephine Dufitinema, Niranjan Sapkota, James W. Kolari
In the era of digitalization, cryptocurrencies have become an alternative asset for both retail and institutional investors. While the emerging digital ecosystem based on blockchain technology offers numerous advantages, it is important to be aware of potential risks such as hacking incidents. In the 2011–2021 period, approximately 1.7 million units of Bitcoin were stolen due to criminal activity with losses exceeding $700 million. This paper models the distribution of stolen coins as a fractal process using power laws to estimate the expected losses from Bitcoin cyberattacks. Our results show that naïve statistics dramatically underestimate the expected loss by more than 70 percent. Our findings have important policy implications with respect to the urgent need for cryptocurrency market oversight by governments and regulatory agencies.
In this paper, we present a methodology for quantifying the decentralization degree of a blockchain network. To accomplish this, we use two well-known graph models of Erdös-Rény and Barabási–Albert in order to study the blockchain network topology. We then quantify the decentralization degree using the clustering coefficient of our network models. We validate our approach through extensive simulations and analyze the decentralization degree with respect to network parameters such as the number of connections per node and the peer selection algorithm. Our results expose the trade-off between the average shortest path and the decentralization degree. Furthermore, we observe the impact of the average shortest path on the network speed and traffic overhead. Finally, we demonstrate that the presence of hub-like nodes such as relay gateways negatively impacts the decentralization degree of blockchain networks.
The purpose of our study is to figure out the transitions of the cryptocurrency market due to the outbreak of COVID-19 through network analysis, and we studied the complexity of the market from different perspectives. To construct a cryptocurrency network, we first apply a mutual information method to the daily log return values of 102 digital currencies from January 1, 2019, to December 31, 2020, and also apply a correlation coefficient method for comparison. Based on these two methods, we construct networks by applying the minimum spanning tree and the planar maximally filtered graph. Furthermore, we study the statistical and topological properties of these networks. Numerical results demonstrate that the degree distribution follows the power-law and the graphs after the COVID-19 outbreak have noticeable differences in network measurements compared to before. Moreover, the results of graphs constructed by each method are different in topological and statistical properties and the network's behavior. In particular, during the post-COVID-19 period, it can be seen that Ethereum and Qtum are the most influential cryptocurrencies in both methods. Our results provide insight and expectations for investors in terms of sharing information about cryptocurrencies amid the uncertainty posed by the COVID-19 pandemic.
The popularity of blockchain technology led to the development of many web platforms with different functionalities. Ethereum, a decentralized, open-source blockchain featuring smart contracts, introduces an interesting ecosystem of human users and autonomous agents (the contracts). It is the most actively used blockchain platform, hosting ether, the second largest cryptocurrency by market capitalization, as its native store of value. The Ethereum blockchain contains a vast amount of user-to-user, user-to-contract, contract-to -user, and contract-to-contract interactions that can be modeled as complex networks. To mine these interactions as graphs through a preferred analytics toolbox, an end-user has to extract, transform, and load (ETL) the required data into the desired network format. However, it is costly and time-consuming to manage the ETL pipeline for the massive and complex blockchain data. To support research in this domain, we develop an end-to-end, automated tool - EtherNet, which performs ETL tasks from a single source of truth (Google BigQuery), and provides graph equivalent representations for visualization and mining on the entire Ethereum blockchain network.
Blockchain technology has gained prominence over the last decade. Numerous achievements have been made regarding how this technology can be utilized in different aspects of the industry, market, and governmental departments. Due to the safety-critical and security-critical nature of their uses, it is pivotal to model the dependability of blockchain-based systems. In this study, we focus on Bitcoin, a blockchain-based peer-to-peer cryptocurrency system. A continuous-time Markov chain-based analytical method is put forward to model and quantify the dependability of the Bitcoin system under selfish mining attacks. Numerical results are provided to examine the influences of several key parameters related to selfish miners’ computing power, attack triggering, and honest miners’ recovery capability. The conclusion made based on this research may contribute to the design of resilience algorithms to enhance the self-defense and robustness of cryptocurrency systems.
With the development of blockchain technology, many new social networks based on blockchain technology have emerged. The unique consensus mechanism and incentive mechanism of blockchain technology makes the law of information propagation in the new social network different from that in the traditional social network. Based on the information propagation characteristics of blockchain social network, this paper considers the influence of opposing groups of opinions, incentive mechanism and user’s conformity psychology in blockchain social network, and uses the evolutionary game to define the transfer process and probability between states and puts forward a new information propagation model. This paper analyses the influence of group density, state transition probability, and incentive policy on information transmission trends in the network through simulation experiments. The comparative experiment with the traditional model shows that the model in this paper can describe the propagation behaviour choices of different propagators under different incentive policies, which the traditional model cannot describe. Using the model in this paper to analyse the information propagation of blockchain social networks can effectively inhibit the propagation of inferior information and further build a good network public opinion environment.
When compared to traditional financial markets, cryptocurrencies were seen as assets with minimal correlations. However, because this continually expanding financial market is marked by substantial volatility and strong price movements over a short period, developing an accurate and reliable forecasting model is deemed crucial for portfolio management and optimization. Given the relevance of cryptocurrencies in the global economy, it is important to determine if Bitcoin (BTC) becomes more predictable as investors adopt more aggressive trading positions. We examine BTC over the period from May 15th, 2021, to April 14th, 2022 (8676-time data), using intraday (hourly) time scales. The results reveal that the random walk hypothesis is rejected at lags of 3 to 16 days, while we see that the BTC market tends toward efficiency (see the evolution between lags of 16 and 2). These findings reveal that, given the uncertainty in the global economy in 2022, namely the Russian invasion of Ukraine, the BTC market shows values of the variance ratios close to unity, implying that it is, apparently, not predictable and that the residuals are not autocorrelated in time. In addition, the results of the Detrended Fluctuation Analysis (DFA) exponent show that this market does not exhibit characteristics of (in) efficiency in its weak form. In other words, this market does not have persistent and mean-reverting properties, thus validating the results of Wright’s Rankings and Signs variance test.
Partial orders and directed acyclic graphs are common data structures that arise naturally in numerous applications, and that define order between data points. Examples are orders of tasks in a project plan, transaction orders in distributed ledgers and execution sequences in computer programs, to mention a few.\nOn the other hand, hierarchical clustering is one of the oldest and most used methods for unsupervised classification and exploratory data analysis. In spite of this, few methods are rigged to take into account the information encoded in the order relation when performing hierarchical clustering of partially ordered data.\nIn his research, Daniel R. Bakkelund has developed new mathematical theory and algorithms to include this information in methods for hierarchical clustering, resulting in the concept of "order preserving hierarchical clustering".\nThe efficacy of theories are demonstrated through experiments on real world data, and show that the in comparison with existing methods, the new methods excel both in cluster quality and order preservation.