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.
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.
Rickard Grassman, Vanessa Bracamonte, Matthew J. Davis, Maki Sato
In this paper, we explore how cryptocurrencies have been received in Sweden and Japan, and what specific attitudes and discourses may reveal about the ethical implications surrounding this new technology. By way of topic modelling prevalent discourses on social media among users of cryptocurrencies, and teasing out the more culturally situated significance in such interactions through discourse analysis, our aim is to unpack the way certain tropes and traces around the notion of autonomy may provide a fruitful lens through which we may discern how this technology has been received in each respective country. The ultimate aim of the paper is to shed light on the attitudes that inform the way this technology is perceived and the cultural and ideological nuances that this brings to the fore, as well as how this culturally nuanced view may help us better discern the potential advantages and ethical challenges associated with this new technology.
In the face of increasingly complex social public opinion situations, the traditional network public opinion governance models have increasingly prominent issues such as information transmission efficiency and information security. In order to establish a new model of fair, credible, and community co-management of network public opinion governance, this article uses blockchain as a key technology for collaborative governance of network public opinion. In order to meet the requirements of network governance, the Delegated Proof-of-Stake(DPoS) algorithm is improved to solve problems. After proposing a reputation model that considers time dynamic factors, this paper constructs a reputation-based voting mechanism and rewards and punishments incentive mechanism, and also designs a new method of counting votes. From the experiment results, it was found that the node participation has increased significantly, the proportion of error nodes was obviously reduced, and the operating efficiency was improved. It shows that the improved consensus algorithm can not only improve the security of the system, reduce the possibility of false public opinion spreading, but also improve the efficiency of information processing, so it can be well applied to information sharing and public opinion governance scenarios.
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,...
Nicolรฒ Vallarano, Matteo Bruno, Emiliano Marchese, Giuseppe Di Trapani ยท 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.
In the blockchain network, to get rewards in the blockchain, blockchain participants pay for various forms of competition such as computing power, stakes, and other resources. Because of the need to pay a certain cost, individual participants cooperate to maintain the longโterm stability of the blockchain jointly. In the course of such competition, the game between each other has appeared invisibly. To better understand the blockchain design of cooperation mechanisms, in this paper, we constructed a game framework between participants with different willingness, using evolutionary game theory, and complex network games. We analyzed how the behavior of participants potentially develops with cost and payoff. We consider the expected benefits of participants for the normal growth of the blockchain as the major factor. Considering the behavior of malicious betrayers, the blockchain needs to be maintained in the early stage. Numerical simulation supports our analysis.
Blockchain and general purpose distributed ledgers are foundational technologies which bring significant innovation in the infrastructures and other underpinnings of our socio-economic systems. These P2P technologies are able to securely diffuse information within and across networks, without need for trustees or central authorities to enforce consensus. In this contribution, we propose a minimalistic stochastic model to understand the dynamics of blockchain-based consensus. By leveraging on random-walk theory, we model block propagation delay on different network topologies and provide a classification of blockchain systems in terms of two emergent properties. Firstly, we identify two performing regimes: a functional regime corresponding to an optimal system function; and a non-functional regime characterised by a congested or branched state of sub-optimal blockchains. Secondly, we discover a phase transition during the emergence of consensus and numerically investigate the corresponding critical point. Our results provide important insights into the consensus mechanism and sub-optimal states in decentralised systems.
In this paper, we investigated the evolution of the External owned account Trading Network(ETN) on Ethereum from August 10th, 2015 to June 9th, 2017 through the perspective of network science. It is showed that the evolution of the topological properties of the ETN and important events are closely related. The average degree of the ETN is not constant but fluctuates within a small range of (2.1, 3.2). And there exists the extremely strongly positive correlation between the number of nodes and the number of edges in the ETN. Moreover, following the events happening, the degree distribution of ETN changes obviously. Moreover, the ETN has smaller values of degree correlation coefficient and clustering coefficient than that of the randomized graph with the same degree sequence, which implies that ETN shows the disassortative mixing pattern by degree and owns few triangles. Through the method of k-shell decomposition, our investigation shows the core of the ETN grows stepwise.
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.
Shahar Somin, Goren Gordon, Alex Pentland, Erez Shmueli ยท 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.
Bitcoin-otc is a peer to peer (over-the-counter) marketplace for trading with bitcoin crypto-currency. To mitigate the risks of the p2p unsupervised exchanges, the establishment of a reliable reputation systems is needed: for this reason, a web of trust is implemented on the website. The availability of all the historic of the users' interaction data makes this dataset a unique playground for studying reputation dynamics through others' evaluations. We analyze the structure and the dynamics of this web of trust with a multilayer network approach distinguishing the rewarding and the punitive behaviors. We show that the rewarding and the punitive behavior have similar emergent topological properties (apart from the clustering coefficient being higher for the rewarding layer) and that the resultant reputation originates from the complex interaction of the more regular behaviors on the layers. We show that the systems' reputation inequality reaches a high steady value with the network evolution. We characterize the reputation trajectories identifying prototypical behaviors associated to three classes of users: trustworthy, untrusted and controversial. Controversial users are the only ones presenting up and down reputation trends. We focus on these cases for understanding which are the possible factors driving reputation falls and which dynamical patterns characterize these cascades: some users have real oscillating behaviors, other abuse of the trust system doing a few good transactions to gain reputation for cheating the users afterwards, other naturally and slowly die out after a long series of positive exchanges (like disappearing from the system) and finally, some users are hardly beaten by organized trolling attacks.
Vladimir Soloviev, Symon P. Yevtushenko, Viktor Batareyev
This article demonstrates the comparative possibility of constructing indicators of critical and crash phenomena in the volatile market of cryptocurrency and developed stock market. Then, combining the empirical cross-correlation matrix with the Random Matrix Theory, we mainly examine the statistical properties of cross-correlation coefficients, the evolution of the distribution of eigenvalues and corresponding eigenvectors in both markets using the daily returns of price time series. The result has indicated that the largest eigenvalue reflects a collective effect of the whole market, and is very sensitive to the crash phenomena. It has been shown that introduced the largest eigenvalue of the matrix of correlations can act like indicators-predictors of falls in both markets.
Andrey Pogudin, Anindya S. Chakrabati, Tiziana Di Matteo
Cryptocurrencies represent an asset class featuring two unique properties: they are not backed by sovereigns, and their supply is fixed exogenously. This combination becomes apparent in their volatility, which is driven only by demand-side factors. In particular, cryptocurrencies represent an extreme case of the excess volatility puzzle, with asset prices moving more than the fundamentals. We explore the effects of market capitalization on the dynamics of cryptocurrencies within both returns and volatility networks and show that these cryptocurrencies exhibit scaling properties in volatility with respect to market capitalization. The dependency network suggests that currencies with a larger market share have a larger presence in the dominant eigenspectrum, and they exert more influence in the comovement network. In these regards, we find parallels between the dynamics of cryptocurrencies and those of more traditional asset classes. Our findings have implications for both researchers and practitioners in terms of modeling and analyzing the collective behavior of financial assets.
Diogo Pacheco, Pik-Mai Hui, Christopher Torres-Lugo, Bao Tran Truong ยท 6 authors
Coordinated campaigns are used to influence and manipulate social media platforms and their users, a critical challenge to the free exchange of information online. Here we introduce a general, unsupervised network-based methodology to uncover groups of accounts that are likely coordinated. The proposed method constructs coordination networks based on arbitrary behavioral traces shared among accounts. We present five case studies of influence campaigns, four of which in the diverse contexts of U.S. elections, Hong Kong protests, the Syrian civil war, and cryptocurrency manipulation. In each of these cases, we detect networks of coordinated Twitter accounts by examining their identities, images, hashtag sequences, retweets, or temporal patterns. The proposed approach proves to be broadly applicable to uncover different kinds of coordination across information warfare scenarios.
Decentralized Finance is the new socioeconomic system growing with an extremely fast pace and changing the way financial interactions are being conducted. In comparison with the growing importance of digital assets and blockchain technology, there is still little understanding of Decentralized Finance as a system. In this thesis we analyze transaction datasets from Bitcoin and Ethereum blockchains to obtain a comprehensive understanding of digital assets -from studying the behaviour of each part to investigating the whole structure and deriving the relations between micro and macro properties of the cryptocurrency systems. Using the Complex Networks approach we explained the system's overall structure and dynamics, and uncovered the mechanism behind network formation. It was found that there is fitness preferential attachment among nodes in the bitcoin network that leads the system to scale-free behaviour. We proposed the quantifiable definition of fitness and supported our finding by simulating a synthetic network and reproducing the main properties of the bitcoin network. After having a good understanding about the structure of the system, we zoom in into its parts by studying the behavioral patterns among the system's users (people). We develop the methodology based on Machine Learning models to define distinct behavioral types in the cryptocurrency systems and find that despite differences between the bitcoin and ethereum systems, there are four common strategies that users follow in both markets. Based on our finding, we model the dynamics of people's behaviour in market as an Absorbing Markov Chain. This approach allowed us to present the behavioral switches in a comprehensive and intuitive way. Moreover, we were able to obtain the predictions on the longevity of users in the system according to their behaviour. Finally, we use the Granger causality test to derive the relations between all system characteristics. We attempt to explain the effect of behavioral switches on the structural properties and price; we find that indeed, switches of users from certain behavioral groups causes a change in price which affects the size of the network as well. We hope that the work and results presented in this thesis will advance the understanding of the new field of Decentralized Finance and expect that the research approach and methodologies developed for this study will be helpful to investigate various complex systems as well.
Vladimir Soloviev, ะกะตัะณัะน ะะปะตะบััะนะพะฒะธั ะกะตะผะตััะบะพะฒ, Victoria Solovieva
The informational (Kolmogorov) measure of complexity in accordance with the Lempel-Ziv algorithm (LZC) is calculated for the logarithmic returns of daily Bitcoin/$ values. The calculations were carried out for a moving window with a variation in its size (50โ250 days) in increments of one day in the framework of the implemented coarse graining procedure. It is shown that in both mono-and multi-scaling versions, LZC is sensitive to noticeable fluctuations in the Bitcoin price that occur as a result of critical events in the cryptocurrency market. In equilibrium, stable state, having a relatively low value, LZC rapidly increases immediately before the crisis, which proves the dominance of the chaotic component of the time series. The classification and periodization of crisis phenomena in the cryptocurrency market for the period 2010โ2020 has been carried out. The results demonstrate the possibility of using the LZC measure as an indicator-precursor of crisis phenomena in the cryptocurrency market.
Ioannis Chalkiadakis, Anna Zaremba, Gareth W. Peters, Michael J. Chantler
This paper establishes a new framework for assessing multimodal statistical causality between cryptocurrency market (cryptomarket) sentiment and cryptocurrency price processes. In order to achieve this, we present an efficient algorithm for multimodal statistical causality analysis based on Multiple-Output Gaussian Processes. Signals from different information sources (modalities) are jointly modelled as a Multiple-Output Gaussian Process, and then using a novel approach to statistical causality based on Gaussian Processes (GPs), we study linear and non-linear causal effects between the different modalities. We demonstrate the effectiveness of our approach in a machine learning application by studying the relationship between cryptocurrency spot price dynamics and sentiment time-series data specific to the crypto sector, which we conjecture influences retail investor behaviour. The investor sentiment is extracted from cryptomarket news data via methods developed in the area of statistical machine learning known as Natural Language Processing (NLP). To capture sentiment, we present a novel framework for text to time-series embedding, which we then use to construct a sentiment index from publicly available news articles. We conduct a statistical analysis of our sentiment statistical index model and compare it to alternative state-of-the-art sentiment models popular in the NLP literature. In regard to the multimodal causality, the investor sentiment is our primary modality of exploration, in addition to price and a blockchain technology-related indicator (hash rate). Analysis shows that our approach is effective in modelling causal structures of variable degree of complexity between heterogeneous data sources and illustrates the impact that certain modelling choices for the different modalities can have on detecting causality. A solid understanding of these factors is necessary to gauge cryptocurrency adoption by retail investors and provide sentiment- and technology-based insights about the cryptocurrency market dynamics.