Damiano Di Francesco Maesa, Andrea Marino, Laura Ricci
The availability of the entire Bitcoin transaction history, stored in its public blockchain, offers interesting opportunities for analysing the transaction graph to obtain insight on users behaviour. This paper presents an analysis of the Bitcoin users graph, obtained by clustering the transaction graph, to highlight its connectivity structure and the economical meaning of the different obtained components. In fact, the bow tie structure, already observed for the graph of the web, is augmented, in the Bitocoin users graph, with the economical information about the entities involved. We study the connectivity components of the users graph individually, to infer their macroscopic contribution to the whole economy. We define and evaluate a set of measures of nodes inside each component to characterize and quantify such a contribution. We also perform a temporal analysis of the evolution of the resulting bow tie structure. Our findings confirm our hypothesis on the components semantic, defined in terms of their economical role in the flow of value inside the graph.
Signed network is a refined form of social network where connections or relationships between people are defined with strength. An edge in this network can contain a positive, negative or neutral sign to define friendly, foe or neutral relationship respectively. This paper aims to utilize the properties of signed network and hence builds a Bitcoin alpha (whom-trust-whom) network. This trading network is further scrutinized to estimate user's reputation i.e. predicting ranks of participating actors. Thus, we are able to provide score to individual user to recommend whether he is safe to make transactions with. In addition, novel method for link prediction by calculating strength of path between two unconnected nodes is proposed. This strength value considers all the features of the nodes, edges and neighbors giving results that have higher accuracy over previously defined methods.
We develop a new approach to temporalizing word2vec-based topic modelling that determines which topics on social media vary with shifts in the phases of a time series to understand potential interactions. This is particularly relevant for the highly volatile bitcoin price with its distinct four phases across 2017-18. We statistically test which words change in frequency between the different stages and compare four word2vec models to assess their consistency in relating connected words in weighted, undirected graphs. For words that fall in frequency when prices shift from rising to falling, all eight topics are identified with the four approaches; for words rising in frequency, three out of the five topics remain constant. These topics are intuitive and match with actual events in the news.
Jelena Mišić, Vojislav B. Mišić, Xiaolin Chang, Saeideh G. Motlagh · 5 authors
In this paper, we provide a comprehensive analytical model for Bitcoin's blockchain distribution network. Components of the model are derived from recent measurements and business analysis reports. We model the data distribution algorithm using branching processes in the network with random distribution of node connectivity. Then, we apply Jackson network model to the entire network in which individual nodes operate as priority M/G/1 queuing systems. Data arrival to the nodes is modeled as a non-homogeneous Poisson process where the distribution of arrival rate to the nodes is derived from the analytical model of data delivery protocol. Within performance results, we present probability distributions of block and transaction distribution time, node response time, forking probabilities, network partition sizes, and duration of ledger's inconsistency period.
Alexandre Bovet, Carlo Campajola, Francesco Mottes, Valerio Restocchi · 7 authors
Cryptocurrencies (the most paradigmatic blockchain-based systems) are distributed systems that allow to exchange tokens among participants.These cryptocurrencies can also be acquired in exchange markets.The availability of the historical bookkeeping of cryptocurrency transfers in a public ledger opens up the possibility of understanding the relationship between aggregate users' behaviour and the cryptocurrency pricing in exchange markets.This paper analyses the properties of the transaction network of Bitcoin.We consider different representations over a period of nine years since its creation and involving 16 million users and 283 million transactions.Importantly, these transactions do not include orders filled in exchange markets, which are settled outside of the blockchain, and ultimately determine Bitcoin price.By analysing these networks, we show the existence of Granger causal relationships between Bitcoin price movements and changes of its transaction network topology.Our results reveal the interplay between structural quantities, indicative of the collective behaviour of Bitcoin users, and price movements, showing that, during price drops, the system is characterised by a larger heterogeneity of users' activity.
Blockchain technology provides a groundbreaking computing paradigm that tackles problems in a completely decentralised manner. As the underlying infrastructure and protocol of blockchain, blockchain networks convey communications and coordination across all involving participants. In extensive application scenarios, conducting community detection over blockchain networks has potential effects on both discovering hidden information and enhancing communicating efficiency. However, the decentralised nature poses a restriction on community detection over blockchain networks. In coping with this restriction, we propose a distributed community detection method based on the Propose-Select-Adjust (PSA) framework that runs in an asynchronous way. We extend the PSA framework using the concept of structural entropy and aim to detect a community structure with low entropy. We test our entropy-based distributed community detection algorithm on both benchmark networks and bitcoin trust networks. Experimental results reveal that our algorithm successfully detects communities with low structural entropy.
Nakamoto's consensus protocol, which is well known for its resistance to sybil attacks by using PoW (Proof of Work), enables us to build public blockchains, such as Bitcoin. In this protocol, miners seek to extend the longest chain by solving blockhash-based cryptographic puzzles and the required time is probabilistically determined. Therefore, the distribution of the time interval affects security, performance and applications which utilize the block height information. Some researchers assumed that the time follows an exponential distribution but this assumption requires that the blockchain network is fully synchronized. To overcome this unreal scenario, the bounded delay model, in which there is an upper bound for block propagation delay on the network, was proposed. However, it is difficult to calculate the upper bound without observing delay and bandwidth on real-world network links. To solve this problem, we proposed another method to analyze the distribution of the longest chain growth time interval by using the observed fork rate. We derived a closed-form lower bound for the CDF (Cumulative Distribution Function) of the time to update the global block height. We also obtained the Pearson distance which can be used as the metric to judge whether the network is approximately synchronous or not. Finally, we conducted network simulations for comparing our lower bound with the lower bound that is based on the bounded delay model. In numerical examples, we show how the block size affects these lower bounds.
Decentralization is a key selling point of most public blockchain platforms. However, despite the widely acknowledged importance of this property, most researches on this topic lack quantification, and none of them performs a calculation on the degrees of decentralization they achieve in practice. In this paper, taking Bitcoin and Ethereum for instances, we propose an entropy method in information theory to quantify the decentralization for them. Using the information entropy, we calculate the discrete degrees of blocks mined and address balances to quantify the degrees of decentralization for Bitcoin and Ethereum systems, and the results of calculations indicate that Bitcoin's mining is more approximately 12% decentralized than Ethereum with full samples, and Bitcoin's wealth is more approximately 9.2% decentralized than Ethereum with 10,000 samples. Our method can be used to evaluate the degree of decentralization for any blockchain system.
Blockchain is a disruptive technology that enables disparate users to share their information in blocks trustworthily without a centralized entity. One fundamental problem is how to stable the block interval. To address this problem, our method is: 1. predict the computing power (i.e., hashrate) of a blockchain system by the cryptocurrency price; 2. stable the interval according to the predicted power. This paper focuses on the prediction of the global computing power. In our prediction, we adopt a LSTM-based regression algorithm to handle the hysteresis of computing power changes in response to the price changes. Taking the Bitcoin system as an example, we run extensive experiments that verify that our prediction algorithm is very accurate.
As an emerging payment method, bitcoin is receiving growing popularity for the different characteristics it shares with conventional fiat currencies. But the pseudonymous nature of bitcoin brings difficulties for regulators to effectively monitor bitcoin-related financial crimes. In this paper, we present an interactive system to visualize the relationship between bitcoin accounts, namely BitVis. With BitVis, users can easily filter transactions on demand, interact with the transaction networks to look for useful information, and analyze behavior of bitcoin accounts. Via BitVis, financial regulators can conveniently track suspicious accounts, while personal investors can easily investigate the activities of an interested account.
We develop a strong diagnostic for bubbles and crashes in Bitcoin, by analysing the coincidence (and its absence) of fundamental and technical indicators. Using a generalized Metcalfe's Law based on network properties, a fundamental value is quantified and shown to be heavily exceeded, on at least four occasions, by bubbles that grow and burst. In these bubbles, we detect a universal super-exponential unsustainable growth. We model this universal pattern with the Log-Periodic Power Law Singularity (LPPLS) model, which parsimoniously captures diverse positive feedback phenomena, such as herding and imitation. The LPPLS model is shown to provide an ex ante warning of market instabilities, quantifying a high crash hazard and probabilistic bracket of the crash time consistent with the actual corrections; although, as always, the precise time and trigger (which straw breaks the camel's back) is exogenous and unpredictable. Looking forward, our analysis identifies a substantial but not unprecedented overvaluation in the price of Bitcoin, suggesting many months of volatile sideways Bitcoin prices ahead (from the time of writing, March 2018).
Ethereum, one of the most popular cryptocurrencies, has attracted increasing attention of people in various fields. As the backbone of Ethereum, its peer-to-peer network has an effect on almost every aspect of the ecosystem. Consequently, it's necessary to understand the topological properties of Ethereum P2P network. In this paper, we conducted a measurement of Ethereum P2P network. Our result shows that the graphs of Ethereum network have a small average shortest path length and a large clustering coefficient, and the degree distribution of nodes does not follow a pure power-law distribution. These indicate that Ethereum network is very close to a small world network. Though there are a large number of stale nodes and useless nodes, Ethereum is still resilient to both random failures and targeted attacks. What's more, we find that there are around one hundred abnormal nodes in the network. The IP addresses of nodes included in the neighbors messages they reply are replaced with their own IP addresses. Those nodes might have a bad influence on network routing.
Motivated by analyzing anonymity properties of Bitcoin network and identification of the origin of illegal transactions, we study the problem of detecting the source node of a transaction message in the Bitcoin network, based on the present spreading model-diffusion. We start by adopting a listening model to get the information of which part of nodes have received the message, say an observation, which is an important premise of solving source detection problem. We propose an estimator for regular trees based on independent multi-reporting observations, and theoretically give a lower bound of the correct detection probability when the observation moment tends to infinity. We show that the more independent reporting observations we have, the higher the probability of detection is, and it further approaches one. The effectiveness of our source estimator is also established in several simulations.
Advanced Steganography and Watermarking Techniques
Recently, graph embedding techniques have been widely used in the analysis of various networks, but most of the existing embedding methods omit the network dynamics and the multiplicity of edges, so it is difficult to accurately describe the detailed characteristics of the transaction networks. Ethereum is a blockchain-based platform supporting smart contracts. The open nature of blockchain makes the transaction data on Ethereum completely public, and also brings unprecedented opportunities for the transaction network analysis. By taking the realistic rules and features of transaction networks into consideration, we first model the Ethereum transaction network as a Temporal Weighted Multidigraph (TWMDG), where each node is a unique Ethereum account and each edge represents a transaction weighted by amount and assigned with timestamp. Then we define the problem of Temporal Weighted Multidigraph Embedding (T-EDGE) by incorporating both temporal and weighted information of the edges, the purpose being to capture more comprehensive properties of dynamic transaction networks. To evaluate the effectiveness of the proposed embedding method, we conduct experiments of node classification on real-world transaction data collected from Ethereum. Experimental results demonstrate that T-EDGE outperforms baseline embedding methods, indicating that time-dependent walks and multiplicity characteristic of edges are informative and essential for time-sensitive transaction networks.
Cryptocurrencies are a novel and disruptive technology that has prompted a new approach to how currencies work in the modern economy. As such, online discussions related to cryptocurrencies often go beyond posts about the technology and underlying architecture of the various coins, to subjective speculations of price fluctuations and predictions. Furthermore, online discussions, potentially driven by foreign adversaries, criminals or hackers, can have a significant impact on our economy and national security if spread at scale.
We determine the number of statistically significant factors in a high dimensional predictive model of cryptocurrencies using a random matrix test. The applied predictive model is of the reduced rank regression (RRR) type; in particular, we choose a flavor that can be regarded as canonical correlation analysis (CCA). A variable selection of hourly cryptocurrencies is performed using the Symbolic estimation of Transfer Entropy (STE) measure from information theory. In simulated studies, STE shows better performance compared to the Granger causality approach when considering a nonlinear system and a linear system with many drivers. In the application to cryptocurrencies, the directed graph associated to the variable selection shows a robust pattern of predictor and response clusters, where the community detection was contrasted with the modularity approach. Also, the centralities of the network discriminate between the two main types of cryptocurrencies, i.e., coins and tokens. On the factor determination of the predictive model, the result supports retaining more factors contrary to the usual visual inspection, with the additional advantage that the subjective element is avoided. In particular, it is observed that the dynamic behavior of the number of factors is moderately anticorrelated with the dynamics of the constructed composite index of predictor and response cryptocurrencies. This finding opens up new insights for anticipating possible declines in cryptocurrency prices on exchanges. Furthermore, our study suggests the existence of specific-predictor and specific-response factors, where only a small number of currencies are predominant.
Beltrán Borja Fiz Pontiveros, Mathis Steichen, Radu State
In this work, we consider the graph of confirmed transactions in bitcoin. Understanding this graph is essential to discern the different economic activities conducted by the pseudonymous actors. In addition to traditional graph analysis methods, new metrics need to be engineered specifically for the bitcoin transaction graph. Hence, we propose a new centrality measure named mint centrality. The measure uses the inherent tree structure of transactions in bitcoin and their relation to the corresponding set of coinbase transactions, and can be evaluated with linear complexity. We present preliminary results of the mint centrality on the first 200,000 blocks of the public bitcoin blockchain.
Tarun Chitra, Monica Quaintance, Stuart Haber, Will Martino
While many distributed consensus protocols provide robust liveness and consistency guarantees under the presence of malicious actors, quantitative estimates of how economic incentives affect security are few and far between. In this paper, we describe a system for simulating how adversarial agents, both economically rational and Byzantine, interact with a blockchain protocol. This system provides statistical estimates for the economic difficulty of an attack and how the presence of certain actors influences protocol-level statistics, such as the expected time to regain liveness. This simulation system is influenced by the design of algorithmic trading and reinforcement learning systems that use explicit modeling of an agent's reward mechanism to evaluate and optimize a fully autonomous agent. We implement and apply this simulation framework to Kadena's Chainweb, a parallelized Proof-of-Work system, that contains complexity in how miner incentive compliance affects security and censorship resistance. We provide the first formal description of Chainweb that is in the literature and use this formal description to motivate our simulation design. Our simulation results include a phase transition in block height growth rate as a function of shard connectivity and empirical evidence that censorship in Chainweb is too costly for rational miners to engage in. We conclude with an outlook on how simulation can guide and optimize protocol development in a variety of contexts, including Proof-of-Stake parameter optimization and peer-to-peer networking design.
Blockchain technology has generated a great deal of interest in recent years, as has the associated area of cryptocurrency trading, not only on the part of individuals but also from traditional financial institutions and hedge funds. However, there is currently limited knowledge as to how to predict future cryptocurrency price movements. This thesis investigates whether online indicators, especially from social media, can be harnessed to predict cryptocurrency price movements – to achieve this, three experiments are conducted. The first experiment analyses time-evolving relationships between chosen online indicators and associated cryptocurrency prices; relationships are considered over short, medium and long-term durations. The work introduces and evaluates several influential factors from the social media platform Reddit, a platform previously unexplored within cryptocurrency prediction literature. It is found that medium and longer-term relationships strengthen in bubble market regimes (compared to non-bubble regimes). The second experiment utilises these promising new factors as inputs to a predictive model. The model used was originally designed to detect influenza epidemic outbreaks, and is repurposed here to model epidemic-like cryptocurrency price bubbles, demonstrating how social media can be used to track the epidemic spread of an investment idea. The predictive power of the model is validated through the generation of a profitable trading strategy. Having considered quantitative count-based metrics in the previous chapters (e.g. posts per day, submissions per day, new authors per day etc.), the next experiment considers the content of social media submissions. More specifically, the third experiment analyses social media submission content to investigate whether certain topics of discussion precede upcoming shorter term (positive or negative) price movements. Information evidencing time-varying interest in various topics is retrieved from social media submissions, upon which hidden interactions with the associated cryptocurrency price are deciphered. It is found that certain topics precede major positive or negative price movements, and also additional analysis shows that certain discussion topics exhibit longer-term relationships with cryptocurrency market prices.
Cryptocurrency is one of the burning issues across the world in the modern era. Literature shows that business analysts always tend to use new technologies and investigate their risks. Some researchers predicted the price fluctuations and investigated the risk of cryptocurrency and blockchain network. However, to the best of our knowledge, there is no study which provides the feasibility information about locations for cryptocurrency investment around the world. This paper aims to provide the aforementioned information to the investors. The proposed methodology is based on Topic modeling along with public opinion mining about cryptocurrencies, blockchain network, bitcoin, litecoin, and ethereum. The other cryptocurrencies are not included in the study because of having insufficient relevant data on the social forums. Top locations are identified such as Australia, Denmark, Netherlands, and the USA etc. Apart from that, the public perception and environment feasibility are also determined. Almost 83.7% tweets of Sweden shows positive sentiment for cryptocurrency investment. Which ranks the highest having friendly environment for cryptocurrency investment. Similarly, the UK shows the least positive perception of cryptocurrency and blockchain technology usage. Some of the significant terms are also determined using Topic modeling from public opinions. Such as authorization rules, investment, profit, volatility, and security etc. These terms are almost identified as subtopic for each of the inquired keyword. This shows the mutual relationship among different cryptocurrencies and public's concerns about cryptocurrencies.
Public interest in cryptocurrencies has consistently risen over the past decade. Owing to this rapid growth, cryptocurrency-related information is being increasingly shared online. As considerable portions of such information in online communities are noise, extracting meaningful information is important. Therefore, judging whose opinion should be considered more important or who the opinion leaders in online communities are is critical. This study analyzed the topics that contain meaningful information, in particular, user groups, by investigating the correlation between topic weights and their price change. The proposed analysis method involves (1) effective classification of the user groups using a hypertext-induced topic selection algorithm, (2) textual information analysis through topic modeling, and (3) the identification of user groups that have a high interest in the Bitcoin price by measuring the correlation between the price and the topics and by measuring the topic similarities between each user group and all users to determine the user group that can effectively represent the entire community. By analyzing the information shared by users, we observed that most users are interested in the price information, whereas users having social influence are not only interested in the price but also in other information.