With the rapid development of blockchain technology, the number of distributed applications continues to increase, so ensuring the security of the network has become particularly important. However, due to its decentralized, ... | Find, read and cite all the research you need on Tech Science Press
Data integrity and security protection are needed in the Internet of Things. IOTA technology with a Directed Acyclic Graph (DAG) structure is a solution to realize secure and scalable data transactions. Recent research IOTA is still faced with the issue of being vulnerable to splitting attacks and left-behind transactions. The splitting attack causes the network to confirm conflict transactions. Then, left-behind transactions cause the network to generate transactions that will not be confirmed. The selection tip weighted random walk (WRW) algorithm uses the Markov Chain Monte Carlo (MCMC) to overcome these two issues by applying the appropriate bias parameter (α). However, when the α is too large, it will produce a lot of left-behind transactions. Determining the optimal value of α is still an important research topic today. An E-IOTA study that gives several α values statically with random selection but can still produce more left-behind transactions than pure WRW. This paper proposes an optimization of the tip selection algorithm (DA-IOTA) to determine the optimal alpha (α) using an approach to dynamically determine each WRW step. The experimental results show that DA-IOTA produces fewer left-behind transactions than MCMC (WRW) and E-IOTA which use α parameters statically.
A general problem with graph data is that it cannot be fed to classical machine learning methods in a straightforward way. Algorithms like logistic regression or decision trees only work well with tabular data. Due to the irregular size of node neighborhoods, raw network data cannot be considered tabular. Recently, several static node embedding models were proposed to learn a vector space representation of network nodes for downstream machine learning tasks. Unfortunately, static graph mining models do not perform well in data-intensive tasks where interactions between network participants are constantly arriving over time. Fitting batch algorithms for large graph snapshots could cause a significant time-delay in the prediction. That is why online graph learning techniques are much preferred in these scenarios. In this thesis, I analyze user interactions in social and cryptocurrency networks with user-related metadata that can be used as ground truth for most of the addressed graph mining tasks. Specifically, we intend to answer the following questions: (1) What are the main advantages of online graph mining techniques over batch models for large-scale social networks and how to best compare their performance? In our research, we focus on graph centrality and node embedding techniques and we propose three online algorithms for these domains. (2) How to mine cryptocurrency networks with novel network science tools to answer open questions in the domain of cryptoeconomics and privacy? By collecting various new Twitter and cryptocurrency network data sets, we were among the first to deploy and analyze node embedding models in several network applications such as vaccine skepticism detection or Ethereum address deanonymization.
Bao Doan, Dulani Jayasuriya, John B. Lee, Jonathan J. Reeves
In this study, we analyse systematic risk associated with the two leading cryptocurrencies - Bitcoin and Ethereum, from 2015 to 2023. Our findings show a significant escalation in the systematic risk levels, with beta estimates rising from 0.032 to 0.834 for Bitcoin, and from 0.087 to 1.003 for Ethereum. This hike in risk levels has dramatically reduced the diversification benefits of cryptocurrency that were documented in prior studies. In addition, we also identify increased autocorrelation of cryptocurrency systematic risk.
This study provides an in-depth analysis of cryptocurrency research, examining the trends, geographical distribution, and future research directions within this rapidly evolving field. Using a comprehensive dataset, we consider whether such research varies by journal ranking. Our findings reveal a significant increase in the number of publications on cryptocurrencies and blockchain technology from pre-2019 to 2022, with a growing interest in diverse digital assets and related concepts. When considering cryptocurrency-based research, we identify prominent keywords such as âSecurity,â âReturn,â âInvestor,â âRisk,â âVolatility,â âCOVID,â and âEthereum,â highlighting the ongoing concerns and interests of researchers in this field. The geographical distribution of author affiliations reveals the prominent role of China and the United States in contributing to cryptocurrency research, with European countries and emerging economies experiencing substantial growth in their number of publications. Based on these findings, we propose several future research directions, including exploring the role of cryptocurrencies in financial inclusion, addressing cybersecurity and privacy concerns, examining the regulatory landscape, and investigating the environmental impact of digital assets. Our study underscores the importance of a multidisciplinary approach to cryptocurrency research, considering the broad societal impacts and implications of this rapidly developing field.
Cryptocurrencies are decentralized digital currencies that use blockchain technology to create a secure and decentralized environment. In the decade since the inception of social media, it has created revolutions and connected people with interests. Social media platforms such as Twitter allow users worldwide to share opinions, emotions, and news. Twitter is one of the most used social media platforms worldwide, where millions of users share tweets continuously every second. By leveraging 1724328 tweets, this research-in-progress paper aims to understand the dynamics of social media usersâ interactions on cryptocurrencies using social set analysis (SSA). The findings reveal that Twitter users are more positive about cryptocurrencies. The analysis also shows an existing relationship between events and the interaction of users, where cryptocurrency-related events shift the emotion, sentiment, and discussion topics of the users. The research-in-progress paper also contributes to demonstrating the effectiveness of the social set analysis framework to analyse and visualize a big social media data.
Cryptocurrency is a speculative investment due to its volatility which could result in significant returns but also could end in crashes. Terra blockchain collapsed when its stable coin UST failed to maintain its peg to 1 USD and caused its sister coin LUNA to drop by more than 90% only in a few days. Terra LUNA itself has gained success and attracted many investors that became a strong community called Lunatics. Using Netnography; this study tried to observe crisis response strategies from Do Kwon, founder and CEO of Terraform Labs, and from Terra LUNA official Twitter account during the crash. Also, this study used community sentiment as an indicator to measure the success of the strategies. In addition, this study observed the interaction of the community during the crash period and how they overcome the crisis together. The results show that mortification and corrective action are the most effective strategy to generate positive sentiment. However, denials toward rumors cause more negative sentiment within the community. Despite the recovery plan from the Terra network, the Lunatics community also has its ways of recovering from the crisis. This study also revealed that community influencers' roles are crucial in controlling rumors during the crisis.
Decentralized finance (DeFi) is known for its unique mechanism design, which applies smart contracts to facilitate peer-to-peer transactions. The decentralized bank is a typical DeFi application. Ideally, a decentralized bank should be decentralized in the transaction. However, many recent studies have found that decentralized banks have not achieved a significant degree of decentralization. This research conducts a comparative study among mainstream decentralized banks. We apply core-periphery network features analysis using the transaction data from four decentralized banks, Liquity, Aave, MakerDao, and Compound. We extract six features and compare the banks' levels of decentralization cross-sectionally. According to the analysis results, we find that: 1) MakerDao and Compound are more decentralized in the transactions than Aave and Liquity. 2) Although decentralized banking transactions are supposed to be decentralized, the data show that four banks have primary external transaction core addresses such as Huobi, Coinbase, and Binance, etc. We also discuss four design features that might affect network decentralization. Our research contributes to the literature at the interface of decentralized finance, financial technology (Fintech), and social network analysis and inspires future protocol designs to live up to the promise of decentralized finance for a truly peer-to-peer transaction network.
This paper inquires into the dynamic imaginaries of the Ethereum project. We present Ethereum as animated by three such imaginaries: the world computer (technical), productive money (economic) and public goods (political). We examine how these imaginaries are materialized, carried forward and evolve through the Ethereum ecosystem, focusing on how Ethereumâs prefigurative logic underpins this dynamism. In our analysis, we pay particular attention to how the imaginaries overlap and often generate contradictions that nonetheless do not seem to undermine the cohesion of the project. We introduce the concept of âprefigurative imaginariesâ to describe how prefiguration works to create multiple, mutually entangled but distinct imaginaries.
Javier Arroyo, David DavĂł, Elena MartĂnez-Vicente, Youssef Faqir-Rhazoui · 5 authors
Decentralized Autonomous Organizations (DAOs) are a new kind of organization that relies on blockchain software to govern their projects. Typically, DAO members may put forward and vote on proposals. For instance these proposals may consist on someone doing some tasks in exchange for a share of the DAO crypto-funds. In recent times, DAOs have gained a remarkable adoption, and yet they are still understudied by the academic literature. In this work, we present a visual analytics tool to study DAO activity focusing on their participation and temporal evolution. Our tool will hopefully help to stimulate research on this new kind of online community and collaborative software.
Community detection is essential in P2P network analysis as it helps identify connectivity structure, undesired centralization, and influential nodes. Existing methods primarily utilize topological data and neglect the rich content data. This paper proposes a technique combining topological and content data to detect communities inside the Bitcoin network using a deep feature representation algorithm and Deep Feedforward Autoencoders. Our results show that the Bitcoin network has a higher clustering coefficient, assortativity coefficient, and community structure than expected from a random P2P network. In the Bitcoin network, nodes prefer to connect to other nodes that share the same characteristics.
Manoel Fernando Alonso Gadi, MiguelâĂngel Sicilia
Abstract Event studies in general rely on having a high-quality curated database of events. In this paper we introduce CryptoGDelt2022, a news event dataset extracted from the Global Database of Events, Language and Tone (GDELT) containing more than 243 thousands cryptocurrency related news events between the 31st of March 2021 and 30th of April 2022. The dataset is enriched with supervised machine learning scores for Relevance, Sentiment and Strength. Supervised Relevance Score measures how related to Cryptocurrency the topic is using news web scrapped from Yahoo in general and from the Cryptocurrency part of the site, after a comparison of approaches; Latent Dirichlet Allocation (LDA), BERT and Naive Bayes, Naive Bayes was chosen and the hyper-parameter tuned model reached accuracy: 97.84 % in the train set and 91.70% in the test set. Supervised Sentiment Score measures the negative, neutral or positive tone of the news, after hyper-parameter tuning, the retrained FinBERT model achieved accuracy of 92.63% in the train set and 86.11% in the test set. Supervised Strength Score measures how strong the news by using the abnormal return using Fama French 3-factor model as target output variable, after hyper-parameter tuning, the trained Naive Bayes model reached accuracy of 63.34%. The work concludes that GDELT is more reliable source of event when compared to news selected from cryptocurrency specialized websites as it presents a more balanced positive and negative number of news. All data sets and Python Jupyter Notebooks are available in the project's GitHub.
Marco Alberto Javarone, Gabriele Di Antonio, Gianni Valerio Vinci, Raffaele Cristodaro · 6 authors
Abstract The behaviour of Bitcoin owners is reflected in the structure and the number of bitcoin transactions encoded in the Blockchain. Likewise, the behaviour of Bitcoin traders is reflected in the formation of bullish and bearish trends in the crypto market. In light of these observations, we wonder if human behaviour underlies some relationship between the Blockchain and the crypto market. To address this question, we map the Blockchain to a spin-lattice problem, whose configurations form ordered and disordered patterns, representing the behaviour of Bitcoin owners. This novel approach allows us to obtain time series suitable to detect a causal relationship between the dynamics of the Blockchain and market trends of the Bitcoin and to find that disordered patterns in the Blockchain precede Bitcoin panic selling. Our results suggest that human behaviour underlying Blockchain evolution and the crypto market brings out a fascinating connection between disorder and panic in Bitcoin dynamics.
Thang Tat Nguyen, Hoang-Nam Dinh, Van-Thanh Nguyen, Do Bao Son · 6 authors
Recently, blockchain technology has been applied in many domains in our life. Blockchain networks typically utilize a consensus protocol to achieve consistency among network nodes in a decentralized environment. Delegated Proof of Stake (DPoS) is a popular mechanism adopted in many networks such as BitShares, EOS, and Cardano because of its speed and scalability advantages. However, votes that come from nodes on a DPoS network tend to support a set of specific nodes that have a greater chance of becoming block producers after voting rounds. Therefore, only a small group of nodes can be selected to become block producers. To address this issue, we propose a new protocol called Evolutionary Computation-based Proof of Criteria (ECPoC), which uses ten criteria to evaluate and select a new block procedure in each round. Next, a set of optimal weights used for maximizing the networkâs decentralization level is identified through the use of evolutionary computation algorithms. The experimental results show that our consensus significantly enhances the degree of decentralization in the selection process of witness nodes compared to DPoS. As a result, ECPoC facilitates fairness between nodes and creates momentum for blockchain network development
Abstract Nowadays, blockchain is an upcoming area for researchers from different research fields. Bitcoin, as the first successful cryptocurrency, has accumulated numerous data after its existence. Here, the Bitcoin transaction graph from a graph theory perspective is investigated with more available data given now. This paper mainly focuses on the transaction graph and provides researchers with both practical and theoretical sides of the data. Several existent measurements and some newer ones are first computed and analysed. These measurements help to interpret the transaction graph more extensively. A new modified BuckleyâOsthus random graph model is proposed, and a simulation of the ChungâLu model is attempted to represent the Bitcoin transaction network. Some suggestions are given to improve the modified BuckleyâOsthus model and point out the pros and cons of these random graph models. Moreover, the experiments show that scaleâfree networks are fundamentally not a good model for Bitcoin transaction networks considering all the data, but the mechanics of preferential attachment is crucial. How to proceed with Bitcoin transaction graph theory from both theoretical and experimental perspectives for future studies is also discussed and analysed.
We have built a bare-metal testbed in order to perform large-scale, reproducible evaluations of erasure coding algorithms. Our testbed supports at least 1000 Ethereum Swarm peers running on 30 machines. Running experimental evaluation is time-consuming and challenging. Researchers must consider the experimental software's limitations and artifacts. If not controlled, the network behavior may cause inaccurate measurements. This paper shares the lessons learned from a bare-metal evaluation of erasure coding algorithms and how to create a controlled-environment in a cluster consisting of 1000 Ethereum Swarm peers.
With the development of blockchain technology, a cryptocurrency based on blockchain technology is becoming more and more popular. The huge cryptocurrency transaction network has therefore received widespread attention. The link prediction learning structure of the network is supportive to understand the mechanism of networks, so it also has been widely studied in the cryptocurrency network. However, the dynamics of cryptocurrency transaction networks have been neglected in past studies. In this study, therefore, we use a graph-regularized method to link past transaction records with future transactions. Based on this, we propose a single latent factor-dependent, nonnegative, multiplicative, and graph regularized-incorporated update (SLF-NMGRU) algorithm and further propose a graph regularized nonnegative latent factor analysis (GrNLFA) model. Eventually, the experimental results on a real cryptocurrency transaction network show that the proposed method improves both the accuracy and computational efficiency.
Francesco Maria De Collibus, Matija PiĆĄkorec, Alberto Partida, Claudio J. Tessone
In this paper, we use the methods of networks science to analyse the transaction networks of tokens running on the Ethereum blockchain. We start with a deep dive on four of them: Ampleforth (AMP), Basic Attention Token (BAT), Dai (DAI) and Uniswap (UNI). We study two types of blockchain addresses, smart contracts (SC), which run code, and externally owned accounts (EOA), run by human users, or off-chain code, with the corresponding private keys. We use preferential attachment and network dismantling strategies to evaluate their importance for the network structure. Subsequently, we expand our view to all ERC-20 tokens issued on the Ethereum network. We first study multilayered networks composed of Ether (ETH) and individual tokens using a dismantling approach to assess how the deconstruction starting from one network affects the other. Finally, we analyse the Ether network and Ethereum-based token networks to find similarities between sets of high-degree nodes. For this purpose, we use both the traditional Jaccard Index and a new metric that we introduce, the Ordered Jaccard Index (OJI), which considers the order of the elements in the two sets that are compared. Our findings suggest that smart contracts and exchange-related addresses play a structural role in transaction networks both in DeFi and Ethereum. The presence in the network of nodes associated to addresses of smart contracts and exchanges is positively correlated with the success of the token network measured in terms of network size and market capitalisation. These nodes play a fundamental role in the centralisation of the supposedly decentralised finance (DeFi) ecosystem: without them, their networks would quickly collapse.
Giordano De Marzo, Francesco Pandolfelli, Vito D. P. Servedio
Blockchains are among the most relevant emerging technologies of recent times and, according to many, they will have a central role in shaping the future of our society. Since the introduction of Bitcoin in 2009, the first notorious blockchain system bound to a cryptocurrency, the blockchain ecosystem has experienced a huge growth, driven by innovations both in conceptual and algorithmic terms, and in the creation of a large number of new cryptocoins. New blockchains and their associated cryptocoins, emerge mostly as the result of forking already existing projects. Here, we show that the appearance of new cryptocoins can be well described by a sub-linear power-law (Heaps' law) of the total crypto-market capitalization. At the same time, we propose a model that well reproduces the evolution of the cryptocurrency ecosystem. Our model suggests that each cryptocurrency triggers, on average, the creation of ca. 1.58 novel cryptocoins, a result confirmed by the analysis of the Bitcoin historical forking tree. Moreover, we deduce that the largest cryptocurrency, nowadays Bitcoin, will comprise around the 50% of the whole crypto-market and that this fraction is going to stabilize in the near future, provided that the present fundamental macro-economic conditions do not change radically.
The Delegated Proof of Stake (DPoS) consensus mechanism uses the power of stakeholders to not only vote in a fair and democratic way to solve a consensus problem, but also reduce resource waste to a certain extent. However, the fixed number of member nodes and single voting type will affect the security of the whole system. In order to reduce the negative impact of the above problems, a new consensus algorithm based on vague set and node impact factors is proposed. We first use fuzzy values to calculate the ratings of all nodes and initially determine the number of agent nodes according to the preset threshold value. Then, we judge whether a secondary screening is needed. If needed, calculating the nodes' impact factor based on their neighboring nodes, and combining their impact factors with adjacency votes to further distinguish the nodes with the same fuzzy value. In addition, we analyze the dynamic changes in the composition and scale of the agent node set and give its ideal size through testing. Finally, we compare the proposed algorithm with DPoS algorithm and existing fuzzy set-based algorithms in different scales and network structures. Results show that no matter in what kind of network structures, the effectiveness of the proposed algorithm is improved. Among which, the most noticeable improvement is seen in complex network structures.
Cryptocurrency based on blockchain technology has gradually become a choice for people to invest in, and several users have participated in the accumulation of massive transaction data. Complete transaction records in blockchains and the openness of data provide researchers with opportunities to mine and analyze data in blockchains. Network modeling and analysis of cryptocurrency transaction records are common methods in blockchain data analysis. The analysis of attribute graphs can provide insights into various economic indicators, illegal activities, and general Internet security, among others. Accordingly, this article aims to summarize and analyze the literature on cryptocurrency transaction data from the perspective of complex networks. To provide systematic guidance for researchers, we put forward a blockchain data analysis framework based on the introduction of the relevant background and reviewed the work from five aspects: blockchain data model, data acquisition on blockchains, existing analysis tools, available insights, and common analysis methods. For each aspect, we introduce the research problems, summarize the methods, and discuss the results and findings. Finally, we present future research points and several open questions in the study of cryptocurrency transaction networks.