Blockchain Papers

Ding Bao, Wei Ren, Yuexin Xiang, Weimao Liu · 7 authors

No abstract is available for this record.

Jakob Svennevik Notland, Mariusz Nowostawski, Jingyue Li

Blockchain systems run consensus rules as code to agree on the state of the distributed ledger and secure the network. Changing these rules can be risky and challenging. In addition, it can often be controversial and take much effort to make all the necessary participants agree to adopt a change. Arguably, Bitcoin has seen centralisation tendencies in pools and in development. However, how these tendencies influence blockchain governance has received minimal community and academic attention. Our study analyses the governmental structures in a blockchain by looking into the history of Bitcoin. We investigate the process of changing consensus rules through a grounded theory analysis comprising quantitative and qualitative data from 34 consensus forks in Bitcoin and Bitcoin Cash. The results reveal the decentralised behaviour in Bitcoin and blockchain. Our results are in contrast to related work, emphasising centralisation among miners and developers. Furthermore, our results show how the consensus-driven deployment techniques and governance of consensus rules are intertwined.

Arad Kotzer, Ori Rottenstreich

Layer-2 is a popular approach to deal with the scalability limitation of blockchain networks. It allows users to execute transactions without committing them to the blockchain by relying on predefined payment channels. Users together with the payment channels form a graph, known as the offchain network topology. Transactions between pairs of users without a connecting channel are also supported through a path of multiple channels. Serving such transactions involves fees paid to intermediate users. In this paper we uncover the potential existence of the Braess paradox in payment networks: Sometimes establishing a new payment channel can increase the fees paid for serving some fixed transactions. We study conditions for the paradox to appear and provide indications for the appearance of the paradox based on real data of Bitcoin's Lightning, a popular layer-2 network. Last, we discuss methods to mitigate the paradox upon establishing a new payment channel.

Chao Li, Balaji Palanisamy, Runhua Xu, Li Duan

Decentralization is widely recognized as a crucial characteristic of blockchains that enables them to resist malicious attacks such as the 51% attack and the takeover attack. Prior research has primarily examined decentralization in blockchains employing the same consensus protocol or at the level of block producers. This paper presents the first individual-level measurement study comparing the decentralization of blockchains employing different consensus protocols. To facilitate cross-consensus evaluation, we present a two-level comparison framework and a new metric. We apply the proposed methods to Ethereum and Steem, two representative blockchains for which decentralization has garnered considerable interest. Our findings dive deeper into the level of decentralization, suggest the existence of centralization risk at the individual level in Steem, and provide novel insights into the cross-consensus comparison of decentralization in blockchains.

Johan Hagelskjar Sjursen, Weizhi Meng, Wei-Yang Chiu

In the current literature, many solutions for solving blockchain scaling have been tried historically, whereas most of them usually may compromise the decentralisation. Ethereum has chosen to scale by switching to Proof of Stake consensus and adding data sharding to allow Layer 2 execution to be cheaper. However, in the light of cross-domain Maximal Extractable Value (MEV), even this strategy may have centralising forces built-in. In this work, we focus on cross-domain MEV and try to identify cross domain arbitrage. In particular, we extract Uniswap data from four different domains and provide an initial analysis of how to identify cross domain arbitrages.

Yue Zhou, Xin Luo, MengChu Zhou

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding (CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure, thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives, thereby promoting further research into this emerging and important field.

Wenrui Zuo, Aravindh Raman, Raúl J. Mondragón, Gareth Tyson

There has been growing interest in the so-called “Web3” movement. This loosely refers to a mix of decentralized technologies, often underpinned by blockchain technologies. Among these, Web3 social media platforms have begun to emerge. These store all social interaction data (e.g., posts) on a public ledger, removing the need for centralized data ownership and management. But this comes at a cost, which some argue is prohibitively expensive. As an exemplar within this growing ecosytem, we explore memo.cash, a microblogging service built on the Bitcoin Cash (BCH) blockchain. We gather data for 24K users, 317K posts, 2.57M user actions, which have facilitated $6.75M worth of transactions. A particularly unique feature is that users must pay BCH tokens for each interaction (e.g., posting, following). We study how this may impact the social makeup of the platform. We therefore study memo.cash as both a social network and a transaction platform.

Haoran Zhu, Xiaolin Chang, Jelena Mišić, Vojislav B. Mišić · 6 authors

Bitcoin is the largest Proof-of-Work (PoW) public blockchain but is vulnerable to various attacks like stubborn mining attack, which greatly downgrades both system throughput and benefits malicious miners (attackers). The existing works assume miners receive new blocks immediately after block generation, which is away from reality. This article aims to quantify the stubborn mining attack severity in an imperfect Bitcoin network in which there exists block receiving delay. In this article, we first develop an analytic model to capture blockchain dynamics, and then derive formulas of both relative revenue and system throughput, which are applied to study attack severity. Experiment results validate our quantitative analysis method and show that imperfect networks favor attackers. Moreover, the results recommend a blockchain system to be composed of small mining pools to get fair revenue distribution, and minimize its network delay and fork probability to get high TPS.

Ruixue Jing, Luis E. C. Rocha

A cryptocurrency is a digital asset maintained by a decentralised system using cryptography. Investors in this emerging digital market are exploring the profitability potential of portfolios in place of single coins. Portfolios are particularly useful given that price forecasting in such a volatile market is challenging. The crypto market is a self-organised complex system where the complex inter-dependencies between the cryptocurrencies may be exploited to understand the market dynamics and build efficient portfolios. In this letter, we use network methods to identify highly decorrelated cryptocurrencies to create diversified portfolios using the Markowitz Portfolio Theory agnostic to future market behaviour. The performance of our network-based portfolios is optimal with 46 coins and superior to benchmarks up to an investment horizon of 14 days, reaching up to 1,066% average expected return within 1 day, with reasonable associated risks. We also show that popular cryptocurrencies are typically not included in the optimal portfolios. Past price correlations reduce risk and may improve the performance of crypto portfolios in comparison to methodologies based exclusively on price auto-correlations. Short-term crypto investments may be competitive to traditional high-risk investments such as the stock market or commodity market but call for caution given the high variability of prices.

Dhanasak Bhumichai, Ryan Benton

Eclipse attacks are considered dangerous potential vulnerabilities of peer-to-peer networks and can cause serious consequences. Detecting eclipse attacks has become a crucial challenge that still lacks comprehensive studies, especially significant characteristics that can efficiently be used to classify eclipse network traffic. To fulfill a research gap, this paper aims to generate new sets of network traffic features that can be efficiently used by machine learning models to detect eclipse attacks by systemically analyzing and synthesizing network traffic features in the Ethereum network layers. After thoroughly analyzing and synthesizing, the newly created features are organized into five categories based on the mechanisms used to manipulate them. The first group is the common network traffic features that can be directly extracted from the blockchain network layers. The second category is the Entropy value of network traffic features that are calculated using an information entropy mechanism to represent the average amount of events in networks. Since the essential characteristics of eclipse attacks are centralized distribution and high probability distribution, the φ-entropy divergence algorithm is deployed to deal with this challenge in the third category. The fourth group is the statistic of the communication of the data package, which implements statistical methods to calculate how packages and data are transmitted via the blockchain networks. The last one is the statistic of data package structures which uses statistical techniques to calculate the characteristics of data packages. Forty-nine characteristics of network traffic features are used to represent the network traffic features in a way that can be easily understood and processed by the learning algorithms in detecting eclipse attacks in the Ethereum blockchain.

Sachith Mankala, Audhav Durai, Anvi Padiyar, Olga Gkountouna · 5 authors

Increasing public concerns about the environment have led to many studies that have explored current issues and approaches towards its protection. Much less studied, however, is topic of public opinion surrounding the impact that cryptocurrencies are having on the environment. The cryptocurrency market, in particular, bitcoin, currently rivals other top well-known assets such as precious metals and exchanged traded funds in market value, and its growing. This work examines public opinion expressed about the environmental impacts of bitcoin derived from Twitter feeds. Three primary research questions were addressed in this work related to topics of public interest, their location, and people and places involved. Our findings show that factions of of the public are interest in protecting the environment, with topics that resonate mainly related to energy. This discourse was also taking place at few similar locations with a mix of different people and places of interest.

Rasoul Amirzadeh, Asef Nazari, Dhananjay Thiruvady, Mong Shan Ee

This study identifies the key factors influencing the price movements of major cryptocurrencies, Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether, using Bayesian networks (BNs). This study addresses two key challenges: modelling price movements in highly volatile cryptocurrency markets and enhancing predictive performance through discretisation-aware Bayesian Networks. It analyses both macro-financial indicators (gold, oil, MSCI, S and P 500, USDX) and social media signals (tweet volume) as potential price drivers. Moreover, since discretisation is a critical step in the effectiveness of BNs, we implement a structured procedure to build 54 BNs models by combining three discretisation methods (equal interval, equal quantile, and k-means) with several bin counts. These models are evaluated using four metrics, including balanced accuracy, F1 score, area under the ROC curve and a composite score. Results show that equal interval with two bins consistently yields the best predictive performance. We also provide deeper insights into each network's structure through inference, sensitivity, and influence strength analyses. These analyses reveal distinct price-driving patterns for each cryptocurrency, underscore the importance of coin-specific analysis, and demonstrate the value of BNs for interpretable causal modelling in volatile cryptocurrency markets.

Jason Zhu, Arijit Khan, Cüneyt Gürcan Akçora

Blockchains are significantly easing trade finance, with billions of dollars worth of assets being transacted daily. However, analyzing these networks remains challenging due to the sheer volume and complexity of the data. We introduce a method named InnerCore that detects market manipulators within blockchain-based networks and offers a sentiment indicator for these networks. This is achieved through data depth-based core decomposition and centered motif discovery, ensuring scalability. InnerCore is a computationally efficient, unsupervised approach suitable for analyzing large temporal graphs. We demonstrate its effectiveness by analyzing and detecting three recent real-world incidents from our datasets: the catastrophic collapse of LunaTerra, the Proof-of-Stake switch of Ethereum, and the temporary peg loss of USDC - while also verifying our results against external ground truth. Our experiments show that InnerCore can match the qualified analysis accurately without human involvement, automating blockchain analysis in a scalable manner, while being more effective and efficient than baselines and state-of-the-art attributed change detection approach in dynamic graphs.

Haaroon Yousaf, Naomi A. Arnold, Renaud Lambiotte, Timothy LaRock · 8 authors

Recent years have witnessed the availability of richer and richer datasets in a variety of domains, where signals often have a multi-modal nature, blending temporal, relational and semantic information. Within this context, several works have shown that standard network models are sometimes not sufficient to properly capture the complexity of real-world interacting systems. For this reason, different attempts have been made to enrich the network language, leading to the emerging field of higher-order networks. In this work, we investigate the possibility of applying methods from higher-order networks to extract information from the online trade of Non-fungible tokens (NFTs), leveraging on their intrinsic temporal and non-Markovian nature. While NFTs as a technology open up the realms for many exciting applications, its future is marred by challenges of proof of ownership, scams, wash trading and possible money laundering. We demonstrate that by investigating time-respecting non-Markovian paths exhibited by NFT trades, we provide a practical path-based approach to fraud detection.

Aos Mulahuwaish, Matthew Loucks, Basheer Qolomany, Ala Al‐Fuqaha

Digital cryptocurrencies such as Bitcoin have exploded in recent years in both popularity and value. By their novelty, cryptocurrencies tend to be both volatile and highly speculative. The capricious nature of these coins is helped facilitated by social media networks such as Twitter. However, not everyone's opinion matters equally, with most posts garnering little to no attention. Additionally, the majority of tweets are retweeted from popular posts. We must determine whose opinion matters and the difference between influential and non-influential users. This study separates these two groups and analyzes the differences between them. It uses Hypertext-induced Topic Selection (HITS) algorithm, which segregates the dataset based on influence. Topic modeling is then employed to uncover differences in each group's speech types and what group may best represent the entire community. We found differences in language and interest between these two groups regarding Bitcoin and that the opinion leaders of Twitter are not aligned with the majority of users. There were 2559 opinion leaders (0.72% of users) who accounted for 80% of the authority and the majority (99.28%) users for the remaining 20% out of a total of 355,139 users.

Paolo Pagnottoni

Inferring the heterogeneous connection pattern of a networked system of multivariate time series observations is a key issue. In finance, the topological structure of financial connectedness in a network of assets can be a central tool for risk measurement. Against this, we propose a topological framework for variance decomposition analysis of multivariate time series in time and frequency domains. We build on the network representation of time–frequency generalized forecast error variance decomposition (GFEVD), and design a method to partition its maximal spanning tree into two components: (a) superhighways, i.e. the infinite incipient percolation cluster, for which nodes with high centrality dominate; (b) roads, for which low centrality nodes dominate. We apply our method to study the topology of shock transmission networks across cryptocurrency, carbon emission and energy prices. Results show that the topologies of short and long run shock transmission networks are starkly different, and that superhighways and roads considerably vary over time. We further document increased spillovers across the markets in the aftermath of the COVID-19 outbreak, as well as the absence of strong direct linkages between cryptocurrency and carbon markets.

Soosan Naderi Mighan, Jelena Mišić, Vojislav B. Mišić

In this paper, we provide a comprehensive analytical model for transaction distribution time in Ethereum derived from the analytical model of gossip data delivery protocol. We introduce three classes of priority for transactions which corresponds to the current Ethereum fee scheme and investigate the impact of transaction priority on response and delivery times using a Jackson network and priority queuing system. Our results show that the delivery times depend on priority and that their distribution is slightly hyper-exponential. We also note anomalous scenarios where lowest priority transactions receive faster service than medium priority ones.

Takumi Hiraide, Shoji Kasahara

In Bitcoin blockchain, miner nodes are likely to choose transactions with high fee to be included in a block. This makes transactions with high fee being processed fast, affecting the amount of transaction fee that users want to pay. The reward for a winning miner consists of transaction fee and newly issued coins, and hence the amount of newly issued coins also affects the miner decision to participate in the mining competition. In addition, mining reward also affects the total hash computing power, which plays an important role of Bitcoin security for reducing the success probability of security attack by a malicious miner. In this paper, we develop a mathematical model for analyzing the interaction between miner decision making and user actions in terms of transaction fees, transaction-confirmation time, and security. We analyze the transaction-inclusion process with queueing theory, while decision making processes of miners and users are analyzed in the context of Nash equilibrium. The numerical examples show how the mining costs and newly issued coins affect miner decision making.

Jing Xu, Yu Sun

Recent years have witnessed the dramatic popularity of cryptocurrencies, in which millions invest to join the cryptocurrency community or make financial gains [1]. Investors employ many ways to analyze a cryptocurrency, from a purely technical approach to a more utility-centred approach [2]. However, few technologies exist to help investors find cryptocurrencies with bright prospects through social metrics, an equally if not more important viewpoint to consider due to the importance of communities in the space. This paper proposes an application to evaluate cryptocurrencies based on social metrics by establishing scores and models with machine learning and other tools [3]. We verified the need for our application through surveys, applied it to test investment strategies, andconducted a qualitative evaluation of the approach. The results show that our tool benefits investors by providing them with a different lens to view cryptocurrencies and helps them make more thorough decisions.

Bishenghui Tao, Hong‐Ning Dai, Haoran Xie, Fu Lee Wang

The metaverse and its underlying blockchain technology have attracted extensive attention in the past few years. How to mine, process, and analyze the tremendous data generated by the metaverse systems has posed a number of challenges. Aiming to address them, we mainly focus on modeling and understanding the blockchain transaction network from a structural identity perspective, which represents the entire network structure and reveals the relations among multiple entities. In this article, we analyze three metaverse-related systems: non-fungible token (NFT), Ethereum (ETH), and Bitcoin (BTC) from the structural-identity perspective. First, we conduct the complex network analysis of the metaverse network and obtain several new insights (i.e., power-law degree distribution, disconnection, disassortativity, preferential attachment, and non-rich-club effect). Secondly, based on such findings, we propose a novel representation learning method named structure-to-vector with random pace (SVRP) for learning both the latent representation and structural identity of the network. Thirdly, we conduct node classification and link prediction tasks with the integration of graph neural networks (GNNs). Empirical results on three real-world datasets demonstrate that our proposed SVRP outperforms other existing methods in multiple tasks. In particular, our SVRP achieves the highest node classification accuracy (Acc) (99.3$\%$) and$F$1-score (96.7$\%$) while only requiring original non-attributed graphs.

Hossein Hassani, Roozbeh Razavi‐Far, Mehrdad Saif, Enrique Herrera‐Viedma

To manage consensus in opinion dynamics models (ODMs), removing bias from agents' interactions and considering their willingness are critical. It can be accomplished by providing a secure mechanism that does not disclose agents' identities and opinions in their interactions, eliminating the impact of opinion similarity on trust building. To build trust and consensus opinion, we propose a linguistic ODM based on the Blockchain technology. This model allows agents' opinions to be expressed usingZ-numbers, as opposed to regular ODMs with numerical opinions. Agents are encouraged to modify their initial opinions in response to a minimum cost consensus model. Willingness of agents to accept or refuse the suggested modifications is realized through a Blockchain regime to avoid bias. The regime, however, must be supported by a trust-building mechanism to persuade agents to alter their opinions. To this end, we propose a Blockchain-enabled trust-building mechanism to improve agents' trust and guide them toward a consensus opinion. Following a sensitivity analysis of the underlying assumptions in the developed model, the proposed ODM is tested for its efficiency and validity.

Shiv Sondhi, Sherif Saad, Kevin Shi, Mohammad Abdullah Al Mamun · 5 authors

Blockchain and distributed ledger technologies rely on distributed consensus algorithms. In recent years many consensus algorithms and protocols have been proposed; most of them are for permissioned blockchain networks. However, the performance of these algorithms is not well understood. This paper introduces an approach to evaluating consensus algorithms and blockchain platforms in a hostile network environment with the presence of byzantine and other network failures. The approach starts by using stochastic modeling to model the behaviors of consensus algorithms under different typical and faulty operational scenarios. Next, we implemented a blockchain application using different consensus protocols and tested their performance using chaos engineering techniques. To demonstrate our generic evaluation approach, we analyze the performance of four permissioned blockchain platforms and their consensus protocols. Our results showed that stochastic modeling is an inexpensive and efficient technique for analyzing consensus protocols. But they do not represent the actual performance of the consensus protocols in a production environment. Moreover, an experiment with chaos engineering indicates that if two different blockchain platforms use the same blockchain algorithm or protocol, we should not assume they will have similar performance. Therefore, It is also essential to consider the role of platform architecture and how the protocols are engineered in a given platform.

Yiting Zhang, Minghao Zhao, Tao Li, Yilei Wang · 5 authors

No abstract is available for this record.

Antonio Cruciani, Francesco Pasquale

The network formation process in the Bitcoin protocol is designed to hide the global network structure: while most of the nodes of the network can be easily discovered, the existence of an edge between two nodes is only known by the two endpoints. In [Becchetti et al., SODA2020] the authors propose a dynamic random graph model inspired by the network formation process in the Bitcoin protocol and they prove that the evolution of the graph quickly terminates and that the resulting graph is an expander, with high probability.