Bitcoin is the first and highest valued cryptocurrency that stores transactions in a publicly distributed ledger called the blockchain. Understanding the activity and behavior of Bitcoin actors is a crucial research topic as they are pseudonymous in the transaction network. In this article, we propose a method based on taint analysis to extract taint flows --dynamic networks representing the sequence of Bitcoins transferred from an initial source to other actors until dissolution. Then, we apply graph embedding methods to characterize taint flows. We evaluate our embedding method with taint flows from top mining pools and show that it can classify mining pools with high accuracy. We also found that taint flows from the same period show high similarity. Our work proves that tracing the money flows can be a promising approach to classifying source actors and characterizing different money flow patterns
Tomas Scagliarini, Giuseppe Pappalardo, Alessio Emanuele Biondo, Alessandro Pluchino · 6 authors
In this paper we analyse the effects of information flows in cryptocurrency markets. We first define a cryptocurrency trading network, i.e. the network made using cryptocurrencies as nodes and the Granger causality among their weekly log returns as links, later we analyse its evolution over time. In particular, with reference to years 2020 and 2021, we study the logarithmic US dollar price returns of the cryptocurrency trading network using both pairwise and high-order statistical dependencies, quantified by Granger causality and O-information, respectively. With reference to the former, we find that it shows peaks in correspondence of important events, like e.g., Covid-19 pandemic turbulence or occasional sudden prices rise. The corresponding network structure is rather stable, across weekly time windows in the period considered and the coins are the most influential nodes in the network. In the pairwise description of the network, stable coins seem to play a marginal role whereas, turning high-order dependencies, they appear in the highest number of synergistic information circuits, thus proving that they play a major role for high order effects. With reference to redundancy and synergy with the time evolution of the total transactions in US dollars, we find that their large volume in the first semester of 2021 seems to have triggered a transition in the cryptocurrency network toward a more complex dynamical landscape. Our results show that pairwise and high-order descriptions of complex financial systems provide complementary information for cryptocurrency analysis.
Social networks have become an inseparable part of human activities. Most existing social networks follow a centralized system model, which despite storing valuable information of users, arise many critical concerns such as content ownership and over-commercialization. Recently, decentralized social networks, built primarily on blockchain technology, have been proposed as a substitution to eliminate these concerns. Since decentralized architectures are mature enough to be on par with the centralized ones, decentralized social networks are becoming more and more popular. Decentralized social networks can offer both common options like writing posts and comments and more advanced options such as reward systems and voting mechanisms. They provide rich eco-systems for the influencers to interact with their followers and other users via staking systems based on cryptocurrency tokens. The vast and valuable data of the decentralized social networks open several new directions for the research community to extend human behavior knowledge. However, accessing and collecting data from these social networks is not easy because it requires strong blockchain knowledge, which is not the main focus of computer science and social science researchers. Hence, our work proposes the SoChainDB framework that facilitates obtaining data from these new social networks. To show the capacity and strength of SoChainDB, we crawl and publish Hive data - one of the largest blockchain-based social networks. We conduct extensive analyses to understand the insight of Hive data and discuss some interesting applications, e.g., game, non-fungible tokens market built upon Hive. It is worth mentioning that our framework is well-adaptable to other blockchain social networks with minimal modification. SoChainDB is publicly accessible at http://sochaindb.com and the dataset is available under the CC BY-SA 4.0 license.
There is an increasing demand for digital crypto-currencies to be more secure and robust to meet the following business requirements: (1) low transaction fees and (2) the privacy of users. Nowadays, Bitcoin is gaining traction and wide adoption. Many well-known businesses have begun accepting bitcoins as a means of making financial payments. However, the susceptibility of Bitcoin networks to information propagation delay, increases the vulnerability to attack of the Bitcoin network, and decreases its throughput performance. This paper introduces and critically analyses new network clustering methods, named Locality Based Clustering (LBC), Ping Time Based Approach (PTBC), Super Node Based Clustering (SNBA), and Master Node Based Clustering (MNBC). The proposed methods aim to decrease the chances of performing a successful double spending attack by reducing the information propagation delay of Bitcoin. These methods embody proximity-aware extensions to the standard Bitcoin protocol, where proximity is measured geographically and in terms of latency. We validate our proposed methods through a set of simulation experiments and the findings show how the proposed methods run and their impact in optimising the transaction propagation delay. Furthermore, these new methods are evaluated from the perspective of the Bitcoin networkâs resistance to partitioning attacks. Numerical results, which are established via extensive simulation experiments, demonstrate how the extensions run and also their impact in optimising the transaction propagation delay. We draw on these findings to suggest promising future research directions for the optimisation of transaction propagation delays.
Open access
Blockchain Technology Applications and Security
Complex Network Analysis Techniques
Advanced Steganography and Watermarking Techniques
Yujing Sun, Hao Xiong, Siu Ming Yiu, KwokâYan Lam
Bitcoin is gaining ever increasing popularity. However, professional skills are required if people want to check bitcoin transaction information from the blockchain. As pointed out in a recent study, there is a lack of tools to support effective interactive investigation of bitcoin transactions. Therefore, we present a novel visualization system,BitAnalysis, for interactive bitcoin wallet investigation. The analytical and visualization functions ofBitAnalysisare defined and developed by following the advice and requirements of a group of entrepreneurs and regulators of bitcoin-related business.BitAnalysisprovides a rich set of functions and intuitive visual interfaces for the users, such as law-enforcement officers and regulators, to effectively visualize and analyze the transactions of a bitcoin wallet (i.e., a cluster of bitcoin addresses) and its related wallets, to track the flow of bitcoins, and to identify wallet correlation using our novel clustering functions. To achieve these functions, we have designed new visualization techniques for presenting bitcoin transactions information and introduced theconnection diagramandbitcoin flow mapas new ways of analyzing, tracking and monitoring the trading activities of a cluster of closely related wallets. We also present an extensive user study that validated the effectiveness and usability ofBitAnalysis.
Cheick Tidiane Bù, Andrea Michienzi, Barbara Guidi, Matteo Zignani · 6 authors
Nowadays, Online Social Media (OSM) are among the most popular web services. Traditional OSM are known to be affected by serious issues including misinformation, fake news, censorship, and privacy violations, to the point that a pressing demand for new paradigms is raised by users all over the world. Among such paradigms, the concepts around the Web 3.0 are fueling a new revolution of online sociality, pushing towards the adoption of innovative and groundbreaking technologies. In particular, the decentralization of social services through the blockchain technology is representing the most valid alternative to current OSM, enabling the development of rewarding strategies for value redistribution, and fake news detection. However, the so-called Blockchain Online Social Media (BOSMs) are far from being mature, with different platforms that continually try to redefine their services in order to attract larger audiences, thus causing blockchain forks and massive user migrations, with the latter dominating the dynamics of the current OSM landscape, too.
Carlos Heras Bernardino, Carlos J. Costa, Manuela AparĂcio
Blockchain is a relatively new technology supported by a decentralized database that received special attention at the research level in the last years due to its fundamental characteristics. Numerous researchers have applied blockchain studies in many fields, but the scope is very large and there is no delimitation of new and emergent trends. The method used to better understand the evolution and impact of blockchain technology was a bibliometric analysis. A search was conducted at digital Elsevierâs database with a single keyword blockchain, and 23383 articles were collected. The study uses cluster analysis method, allowing to construct and visualize bibliometric networks and to build co-occurrences networks. The results indicate that there are positive correlations between countries, that China and the United States are part of the most influential cluster. Indicates also emerging developments in the areas of governance, industry, decision-making processes, management, internet of things, information security, and a new hot topic, energy.
Jacques Bou Abdo, Shuvalaxmi Dass, Basheer Qolomany, Liaquat Hossain
Abstract The world economy is experiencing the novel adoption of distributed currencies that are free from the control of central banks. Distributed currencies suffer from extreme volatility, and this can lead to catastrophic implications during future economic crisis. Understanding the dynamics of this new type of currencies is vital for empowering supervisory bodies from current reactive and manual incident responders to more proactive and well-informed planners. Bitcoin, the first and dominant distributed cryptocurrency, is still notoriously vague, especially for a financial instrument with market value exceeding $1 trillion. Modeling of bitcoin overlay network poses a number of important theoretical and methodological challenges. Current measuring approaches, for example, fail to identify the real network size of bitcoin miners. This drastically undermines the ability to predict forks, the suitable mining difficulty and most importantly the resilience of the network supporting bitcoin. In this work, we developed Evolutionary Random Graph, a theoretical model that describes the network of bitcoin miners. The correctness of this model has been validated using simulated and measure bitcoin data. We then predicted forking, optimal mining difficulty, network size and consequently the networkâs inability to stand a drastic drop in bitcoin price using the current mining configuration.
We investigate logarithmic price returns cross-correlations at different time horizons for a set of 25 liquid cryptocurrencies traded on the FTX digital currency exchange. We study how the structure of the Minimum Spanning Tree (MST) and the Triangulated Maximally Filtered Graph (TMFG) evolve from high (15 s) to low (1 day) frequency time resolutions. For each horizon, we test the stability, statistical significance and economic meaningfulness of the networks. Results give a deep insight into the evolutionary process of the time dependent hierarchical organization of the system under analysis. A decrease in correlation between pairs of cryptocurrencies is observed for finer time sampling resolutions. A growing structure emerges for coarser ones, highlighting multiple changes in the hierarchical reference role played by mainstream cryptocurrencies. This effect is studied both in its pairwise realizations and intra-sector ones.
Microblogging has become an extremely popular communication tool among Internet users worldwide. Millions of users daily share a huge amount of information related to various aspects of their lives, which makes the respective sites a very important source of data for analysis. Bitcoin (BTC) is a decentralized cryptographic currency and is equivalent to most recurrently known currencies in the way that it is influenced by socially developed conclusions, regardless of whether those conclusions are considered valid. This work aims to assess the importance of Twitter usersâ profiles in predicting a cryptocurrencyâs popularity. More specifically, our analysis focused on the user influence, captured by different Twitter features (such as the number of followers, retweets, lists) and tweet sentiment scores as the main components of measuring popularity. Moreover, the Spearman, Pearson, and Kendall Correlation Coefficients are applied as post-hoc procedures to support hypotheses about the correlation between a user influence and the aforementioned features. Tweets sentiment scoring (as positive or negative) was performed with the aid of Valence Aware Dictionary and Sentiment Reasoner (VADER) for a number of tweets fetched within a concrete time period. Finally, the Granger causality test was employed to evaluate the statistical significance of various features time series in popularity prediction to identify the most influential variable for predicting future values of the cryptocurrency popularity.
Social media and financial markets are ecosystems bound to interact and overlap more and more. For example, cryptocurrencies, Bitcoin in the first place, have long been among the hottest topics on social media. Here we illustrate a methodology for correlating Bitcoin price trends and social media that operates on the aggregation of news from multiple social networks as typically occurs on dedicated channels in sites such as Reddit. For this purpose, we define general laws to map the financial fluctuations of Bitcoin in a space defined on three dimensions: content volume, sentiment and time. These laws provide the foundation upon which to build effective methodologies for social media-based prediction of Bitcoin performance.
Victor Chang, Karl Hall, Qianwen Xu, Le Minh Thao Doan · 5 authors
This paper applies social network analysis in two experiments. In the first experiment, social network analysis is conducted on student friendship networks to find relational patterns. Then, three community detection methods are used to divide the student network. The RSiena package is used to illustrate the coevolution of friendship networks with smoking and drinking behavior. In this experiment, it was determined that in the closed network, same-sex reciprocated relationships are preferred. The second experiment analyzes a weighted trust network that involves users trading with Bitcoin on the BTC-Alpha platform. Since the dealers of Bitcoin are anonymous, there is an urgent need to record every dealerâs credit history to prevent fraud and other security problems. The second experiment aims to improve security problems within the Bitcoin trust network by applying social network analysis.
Adrian Barradas, Acela Tejeda-Gil, Rosa MarĂa CantĂłn Croda
Cryptocurrencies have recently emerged as financial assets that allow their users to execute transactions in a decentralized manner. Their popularity has led to the generation of huge amounts of data, specifically on social media networks such as Twitter. In this study, we propose an iterative kappa architecture that collects, processes, and temporarily stores data regarding transactions and tweets of two of the major cryptocurrencies according to their market capitalization: Bitcoin (BTC) and Ethereum (ETH). We applied a k-means clustering approach to group data according to their principal characteristics. Data are categorized into three groups: BTC typical data, ETH typical data, BTC and ETH atypical data. Findings show that activity on Twitter correlates to activity regarding the transactions of cryptocurrencies. It was also found that around 14% of data relate to extraordinary behaviors regarding cryptocurrencies. These data contain higher transaction volumes of both cryptocurrencies, and about 9.5% more social media publications in comparison with the rest of the data. The main advantages of the proposed architecture are its flexibility and its ability to relate data from various datasets.
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.
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.
We analyzed 23 mining pools and explore the mobility of miners throughout Bitcoinâs 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 Bitcoinâs 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 2011â2021 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 naĂŻve 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.
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.
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 minersâ computing power, attack triggering, and honest minersâ 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 userâs 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.