Alvin Heng Jun Ren, Ling Feng, Siew Ann Cheong, Rick Siow Mong Goh
Off-chain transaction handling like the Lightning Network (LN) is among the most promising solutions to solve the scaling challenges in blockchain technology like Bitcoin. At the same time, the LN faces its own challenges like transaction path lengths, centralization of channels (hubs), channel imbalances (depletion), etc. Here we study the effects of payment channel fees on these various factors. To get realistic insights, we based our study on empirical Bitcoin transaction patterns and existing LN structure, and apply a simple form of fee structure with only one tunable parameter, α, that is most influential on the transaction routing paths. We assume the transactions through the LN take the path of the lowest aggregate fees, and found as a consequence that one cannot have short average path lengths and low overall channel imbalances at the same time. A good compromise is to have fees proportional to the square root of the channel capacity, such that reasonably short path lengths and overall balanced channel capacities can be achieved that makes the operation of the LN more sustainable.
The pseudonymous nature of Bitcoin has sparked the twin rivaling researches in Bitcoin community, that is, either protecting or attacking anonymity. In spite of this intense battle, the answer to a primary question is absent – Do Bitcoin users themselves care about anonymity? This paper demystifies this doubt via analyzing the Bitcoin transaction graphs with the following three contributions: 1). We outline three representative metrics that can signify whether users concern about anonymity. 2). We examine the collective trend of anonymity concerns from a macroscope. 3). We pay particular attention on critical addresses in a microscope to unveil their anonymity concerns.This paper arrives at both expected conclusions and unexpected surprises. In particular, the expected ones are: rich addresses concern more about anonymity than poor ones. Miner addresses start caring about anonymity when exchange rate soars. Stock addresses never hide their intent of jump-and-dump. The surprises are: the majority of the users show weak concerns on anonymity. One can easily find both hot and cold wallet addresses owned by big organizations.
Marc Jourdan, Sébastien Blandin, Laura Wynter, Pralhad Deshpande
The Bitcoin transaction graph is a public data structure organized as transactions between addresses, each associated with a logical entity. In this work, we introduce a complete probabilistic model of the Bitcoin Blockchain, setting the basis for follow-up AI applications on Bitcoin transactions. We first formulate a set of conditional dependencies induced by the Bitcoin protocol at the block level and derive a corresponding fully observed graphical model of a Bitcoin block. We then extend the model to include hidden entity attributes such as the functional category of the associated logical agent and derive asymptotic bounds on the privacy properties implied by this model. At the network level, we show evidence of complex transaction-to-transaction behavior and present a relevant discriminative model of the agent categories. Performance of both the block-based graphical model and the network-level discriminative model are evaluated on a subset of the public Bitcoin Blockchain.
The digital payment system that uses cryptocurrency, such as Bitcoin, is a distributed ledger working on a peer-to-peer network. We present a method to make a scale-free network for such applications. Using some biased physical quantities that are observable in sites, we can make the scale-free network through processes of cooperating distributed sites. Each node only proposes connecting to the more attractive node among randomly known nodes. The candidate node that is found by each node agrees to set two-way links if the requesting node is more attractive than the old node that is already connected. Once they establish the new bidirectional relationship, they, respectively, remove the outgoing link to the less attractive node. We analytically calculate the connectivity distribution and show that the scaling exponent is 2.5. By Monte Carlo simulations, we confirm that a power law distribution of the scaling exponent 2.5 describes the degree distribution of the topology.
Rubaiyat Islam, Yoshi Fujiwara, Shinya Kawata, Hiwon Yoon
In a closed economic system like blockchain, the total amount of generated cryptocurrency called bitcoin is conserved and the transaction patterns demonstrate an insight of money flow inside the blockchain. For the last 2 years, bitcoin market has grabbed an immense attention from the investors, technology entrepreneurs and currency enthusiasts. In this paper, we have come up with some findings in our investigation about the bitcoin time-series transaction patterns. We have graphically represented bitcoin’s weekly patterns as a real economic currency that has been minted, stored and exchanged inside the bitcoin blockchain network. We identified outliers’ activities with the help of descriptive statistical analysis. We also demonstrated transaction pattern behavioral change. The main implication of these findings is to understand some stylized facts of the time-series transaction of cryptocurrency-based fully digital financial system. Besides in our analysis, we have shown that the behavioral change of the transaction pattern is capable of explaining the system development events or major historical events that have a network impact.
Cryptocurrency exchange platforms, which have appeared recently, provide exchanges between different types of cryptocurrencies as well as fiat money. Since multiple exchange platforms exist today, it is of essential importance to have timely information about user experience and the behavior of participants in such platforms. Trust is a notion that has been studied extensively in sociology, psychology, economics and computer science, with an objective to provide measures for quantifying user behavior in specific social settings and contexts. While in the domain of online social networks, the trust has been thoroughly analyzed from various points of view, in cryptocurrency exchange platforms, the questions about quantifying the levels of emerging trust, are still open. In this work, we elaborate on quantifying trust in the context of cryptocurrency exchange platforms. We focus on two well known bitcoin exchange platforms: Bitcoin-OTC and Bitcoin-Alpha. Using the existing data about user ratings upon interactions in these two platforms, we show how trust is related with the network structure and how to identify communities where trust level is high among participants. Our results can serve as a basis for a strategy on how to interact within these platforms.
Eaman Jahani, P. M. Krafft, Yoshihiko Suhara, Esteban Moro · 5 authors
Participants in cryptocurrency markets are in constant communication with each other about the latest coins and news releases. Do these conversations build hype through the contagiousness of excitement, help the community process information, or play some other role? Using a novel dataset from a major cryptocurrency forum, we conduct an exploratory study of the characteristics of online discussion around cryptocurrencies. Through a regression analysis, we find that coins with more information available and higher levels of technical innovation are associated with higher quality discussion. People who talk about "serious" coins tend to participate in discussion displaying signatures of collective intelligence and information processing, while people who talk about "less serious" coins tend to display signatures of hype and naïvety. Interviews with experienced forum members also confirm these quantitative findings. These results highlight the varied roles of discussion in the cryptocurrency ecosystem and suggest that discussion of serious coins may be oriented towards earnest, perhaps more accurate, attempts at discovering which coins are likely to succeed.
We showcase a graph mining tool, BiVA, for visualization and analysis of the Bitcoin network. It enables data exploration, visualization of subgraphs around nodes of interest, and integrates both standard and new algorithms, including a general algorithm for flow based clustering for directed graphs, and other Bitcoin network specific wallet address aggregation mechanisms. The BiVA user interface makes it easy to get started with a basic visualization that gives insights into nodes of interests, and the tool is modular, allowing easy integration of new algorithms. Its functionalities are demonstrated with a case study of extortion of Ashley Madison data breach victims.
Bitcoin has recently been labelled as a “dangerous speculative bubble†by Nobel Prize-winning economists Joseph Stiglitz and Robert Shiller, as the Bitcoin's market value now exceeds the GDP of over 130 countries. In this study, the multifractality and efficiency of the Bitcoin price index are tested, using a nonlinear data analysis technique called the multifractal detrended fluctuation analysis (MF-DFA). In addition, we assess the time-variations in the market efficiency level through using a rolling-window framework. Our evidence shows that the efficiency of the Bitcoin market changes over time and this market seems to be more efficient during downward than upward periods. We also find that Bitcoin is marked by a persistent long memory phenomenon in its short- term components, which could be interpreted as a possible speculation by investors.
The bitcoin price has surged in recent years and it has also exhibited phases of rapid decay. In this paper we address the question to what extent this novel cryptocurrency market can be viewed as a classic or semi-efficient market. Novel and robust tools for estimation of multi-fractal properties are used to show that the bitcoin price exhibits a very interesting multi-scale correlation structure. This structure can be described by a power-law behavior of the variances of the returns as functions of time increments and it can be characterized by two parameters, the volatility and the Hurst exponent. These power-law parameters, however, vary in time. A new notion of generalized Hurst exponent is introduced which allows us to check if the multi-fractal character of the underlying signal is well captured. It is moreover shown how the monitoring of the power-law parameters can be used to identify regime shifts for the bitcoin price. A novel technique for identifying the regimes switches based on a goodness of fit of the local power-law parameters is presented. It automatically detects dates associated with some known events in the bitcoin market place. A very surprising result is moreover that, despite the wild ride of the bitcoin price in recent years and its multi-fractal and non-stationary character, this price has both local power-law behaviors and a very orderly correlation structure when it is observed on its entire period of existence.
The Bitcoin protocol prevents the occurrence of double-spending (DS), i.e. the utilization of the same currency unit more than once. At the same time a DS attack, where more conflicting transactions are generated, might be performed to defraud a user, e.g. a merchant. Therefore, in this work, we propose a model for detecting the presence of conflicting transactions by means of an 'oracle' that polls a subset of nodes of the Bitcoin network. We assume that the latter has a complex structure. So, we investigate the relation between the topology of several complex networks and the optimal amount, and distribution, of a subset of nodes chosen by the oracle for polling. Results show that small-world networks require to poll a smaller amount of nodes than regular networks. In addition, in random topologies, a small number of polled nodes can make a detection system fast and reliable even if the underlying network grows.
Cryptocurrency is a well-developed blockchain technology application that is currently a heated topic throughout the world. The public availability of transaction histories offers an opportunity to analyze and compare different cryptocurrencies. In this paper, we present a dynamic network analysis of three representative blockchain-based cryptocurrencies: Bitcoin, Ethereum, and Namecoin. By analyzing the accumulated network growth, we find that, unlike most other networks, these cryptocurrency networks do not always densify over time, and they are changing all the time with relatively low node and edge repetition ratios. Therefore, we then construct separate networks on a monthly basis, trace the changes of typical network characteristics (including degree distribution, degree assortativity, clustering coefficient, and the largest connected component) over time, and compare the three. We find that the degree distribution of these monthly transaction networks cannot be well fitted by the famous power-law distribution, at the same time, different currency still has different network properties, e.g., both Bitcoin and Ethereum networks are heavy-tailed with disassortative mixing, however, only the former can be treated as a small world. These network properties reflect the evolutionary characteristics and competitive power of these three cryptocurrencies and provide a foundation for future research.
Ee Hong Aw, Ralucca Gera, Kenneth S. Hicks, Nicholas Koeppen · 5 authors
Due to the anonymous and remote nature of Electronic Commerce (E-commerce), reviews of products and vendors left by previous customers have emerged as an integral part of most online transactions. The reviews may influence the decision of customers buying the product since E-commerce websites/services do not allow customers to validate and inspect products in-store. In this paper, we analyze data from two BITCOIN marketplaces which include transactions between marketplace users and the ratings of those transactions given by those users. In this analysis we create a synthetic network model with similar topological properties as the networks of the interactions of both marketplaces. The results of our analysis show an interesting phenomenon in which user ratings, which range from -10 to 10, converge to a value of approximately two as the number of a user's transactions increase. Finally, we suggest future work on our synthetic model to improve its agreement with the transaction networks in order to better understand how reviews influence user decisions on transactions.
This paper deals with the design of the secure blockchain network framework to prevent damages from an attacker. The decentralized network design called the Blockchain Governance Game is a new hybrid theoretical model and it provides the stochastic game framework to find best strategies towards preparation for preventing a network malfunction by an attacker. Analytically tractable results are obtained by using the fluctuation theory and the mixed strategy game theory. These results enable to predict the moment for operations and deliver the optimal portion of backup nodes to protect the blockchain network. This research helps for whom considers the initial coin offering or launching new blockchain based services with enhancing the security features.
Taking Ei Compendex (EI) and China National Knowledge Infrastructure (CNKI) databases as the literature sources, this paper presented a bibliographic analysis of the blockchain-related literature between January 2011 and September 2017. For each literature source, we built a separate dataset. Authors' productivity and collaboration, affiliation of authors and collaboration amongst institutions were analyzed using techniques of social networks analysis on both datasets. According to the results, the CNKI authors/institutes raised their productivity and outperformed the EI authors/institutes since 2016. However, the EI authors/institutes show a higher level than the CNKI authors/institutes in collaboration. We also summarized the hot topics on the EI dataset using textual analysis and discovered researchers have shifted their attention from Bitcoin itself to the blockchain technology underlying it.
Bitcoin, a decentralized P2P cryptocurrency, is the most successful application of blockchain that has gained widespread attention over the past several years. It allows users to create individual transactions and any number of addresses, which is used to send and accept Bitcoin. Generally, the value of Bitcoin is mainly reflected in the degree of users' participation and recognition. In the past, there are many proposed research successfully clustering various addresses into different groups to help identify specific users by using two heuristics. However, they paid little attention to exploit the users' activeness and possible impacts on the development of Bitcoin system. Participation ranking as an indicator is important for enhancing Proof-of-Activity based algorithm and predicting the activity of blockchain system. In this paper, we proposed a model to evaluate users' participation and importance objectively and mechanically by adapting PageRank algorithm. We finally run experiments on the public available metadata of blockchain and compare the results with some well-known addresses to assess its competitiveness.
Blockchain technology, which has been known by mostly small technological circles up until recently, is bursting throughout the globe, with a potential economic and social impact that could fundamentally alter traditional financial and social structures. Issuing cryptocurrencies on top of the Blockchain system by startups and private sector companies is becoming a ubiquitous phenomenon, inducing the trading of these crypto-coins among their holders using dedicated exchanges. Apart from being a trading ledger for tokens, Blockchain can also be observed as a social network. Analyzing and modeling the dynamics of the "social signals" of this network can contribute to our understanding of this ecosystem and the forces acting within in. This work is the first analysis of the network properties of the ERC20 protocol compliant crypto-coins' trading data. Considering all trading wallets as a network's nodes, and constructing its edges using buy--sell trades, we can analyze the network properties of the ERC20 network. Examining several periods of time, and several data aggregation variants, we demonstrate that the network displays strong power-law properties. These results coincide with current network theory expectations, however nonetheless, are the first scientific validation of it, for the ERC20 trading data. The data we examined is composed of over 30 million ERC20 tokens trades, performed by over 6.8 million unique wallets, lapsing over a two years period between February 2016 and February 2018.
Alexandre Bovet, Carlo Campajola, Jorge F. Lazo, Francesco Mottes · 10 authors
The functioning of the cryptocurrency Bitcoin relies on the open availability of the entire history of its transactions. This makes it a particularly interesting socio-economic system to analyse from the point of view of network science. Here we analyse the evolution of the network of Bitcoin transactions between users. We achieve this by using the complete transaction history from December 5th 2011 to December 23rd 2013. This period includes three bubbles experienced by the Bitcoin price. In particular, we focus on the global and local structural properties of the user network and their variation in relation to the different period of price surge and decline. By analysing the temporal variation of the heterogeneity of the connectivity patterns we gain insights on the different mechanisms that take place during bubbles, and find that hubs (i.e., the most connected nodes) had a fundamental role in triggering the burst of the second bubble. Finally, we examine the local topological structures of interactions between users, we discover that the relative frequency of triadic interactions experiences a strong change before, during and after a bubble, and suggest that the importance of the hubs grows during the bubble. These results provide further evidence that the behaviour of the hubs during bubbles significantly increases the systemic risk of the Bitcoin network, and discuss the implications on public policy interventions.
Bitcoins and Blockchain technologies are attracting the attention of different scientific communities. In addition, their widespread industrial applications and the continuous introduction of cryptocurrencies are also stimulating the attention of the public opinion. The underlying structure of these technologies constitutes one of their core concepts. In particular, they are based on peer-to-peer networks. Accordingly, all nodes lie at the same level, so that there is no place for privileged actors as, for instance, banking institutions in classical financial networks. In this work, we perform a preliminary investigation on two kinds of network, i.e. the Bitcoin network and the Bitcoin Cash network. Notably, we analyze their global structure and we try to evaluate if they are provided with a small-world behavior. Results suggest that the principle known as 'fittest-gets-richer', combined with a continuous increasing of connections, might constitute the mechanism leading these networks to reach their current structure. Moreover, further observations open the way to new investigations into this direction.