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.
The interaction between the increased complexity of information management applications and the demand for those applications has not been completely understood. Still, it is essential to researchers in business and information management, given the intense focus on improving efficiency and profitability in the future. In the last few years, some of the main information management applications and events that have increased the complexity of information technology processes include the mining of cryptocurrencies. With the growth of new business transactions using cryptocurrencies and the enormous number of algorithms used by miners to mine cryptocurrencies, the complexity of processes and network congestion has become a factor to consider in the future. While most of the studies related to this topic have focused on the technical side of the mining of cryptocurrencies, this study focuses on the evolution of complexity in information management processes and looks into the future profitability of cryptocurrencies.
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 “Security,” “Return,” “Investor,” “Risk,” “Volatility,” “COVID,” and “Ethereum,” 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 users’ 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.
Online social networks (OSNs) revolutionized how people interact with each other, and nowadays, thanks to blockchain technology, new solutions are being considered, giving birth to blockchain online social media (BOSMs). BOSMs use the blockchain to redistribute with their users the wealth generated by the platform through a rewarding system, assigning better rewards to socially impactful users. Thus, these new systems are characterized by highly intertwined economical and social aspects and constitute a new scenario in the world of social networks. Many scenarios, economic and social alike, show a phenomenon known as “the rich-get-richer,” which states that the richest actors of a system tend to become richer over time. To the best of our knowledge, in the scenario of BOSMs, where users can acquire cryptocurrency through their social actions, this type of phenomenon was not yet studied. In this article, we propose a methodological framework composed of three hypotheses that can help study the rich-get-richer phenomenon through a set of measures and indices. In addition, we apply the proposed framework to the Steem case study, showing how unevenly wealth is distributed on its blockchain and comparing our results to other scenarios.
The growing acceptance and popularity of cryptocurrencies have boosted the digital financial markets, which have also increased crime risk due to their anonymity and decentralization. Appropriately monitoring decentralized cryptocurrency, particularly Bitcoin, can prevent participants from financial loss and benefit the community. Therefore, in this paper, we build the first Bitcoin address subgraph dataset called BASD-8, which contains 3,830 labeled Bitcoin address subgraphs, and we study the structural characteristics of these subgraphs, aiming at identifying eight common types of Bitcoin addresses to distinguish between normal and abnormal addresses. Three methods are utilized to exploit subgraph patterns: complex network, machine learning, and empirical analysis. Specifically, we calculate ten vital metrics of subgraphs as features to train address classifiers using basic machine learning models. Also, a graph neural network model is trained as a graph-level classifier, and the experimental results with the best f1-score of 91.35% illustrate the effectiveness of our dataset and study methods. Furthermore, we conduct a detailed empirical pattern analysis combining the subgraph structures and the definitions of each category of Bitcoin addresses.
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.
The collective behaviors of community members in dynamic bitcoin transaction network are significant to understand the evolutionary characteristics of communities for bitcoin transaction network. In this paper, we empirically investigate the behavior evolution of new nodes forming communities for the bitcoin transaction network. First, we divide the bitcoin transaction network into multiple time segments, and detect community on each time segment. Then, according to the set similarity method, we mark the community with maximal similarity [Formula: see text] at adjacent timestamps as the new community. Finally, we propose an evolution index to illustrate the evolution trend of new nodes forming communities, and introduce the reshuffle model to compare with it. The results show that there are obvious differences in the early stage, and new traders tend to join new communities. However, after August 2011, the trends of before and after reorganization are very similar, which indicates that in bitcoin trading, the behaviors of new traders forming communities become random. Our work may be helpful for the understanding of user behavior characteristics in bitcoin trading, and provide a new perspective for the research of bitcoin transaction network.
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.
Fengyang Guo, Xun Xiao, Artur Hecker, Schahram Dustdar
IOTA blockchain is a new type of distributed ledger systems that is lightweight without mining and feeless-of-using. Rather than using a chain structure as in traditional blockchains, IOTA organizes ledger records with a directed acyclic graph (DAG), called Tangle. When message entries are committed into the ledger, the ledger tangle grows in a special way where multiple messages could be attached by different processing nodes in parallel. Such a unique evolution process motivates us to study the ledger tangle dynamics, which is unexplored so far. In this paper, we present the first generative modeling for IOTA tangle based on stochastic analysis. A key finding is that IOTA tangle renders a double Pareto Lognormal (dPLN) distribution, rather not typical network models (e.g., Power-Law and Exponential distributions). Quantitative comparisons show that the fitting quality of our model outperforms existing popular models on official real world datasets published by IOTA Foundation. Estimated model parameters are provided, which is immediately instrumental for a more realistic IOTA network generator design. The proposed generative model also provides a deeper understanding of the internal mechanics of IOTA network.
Soosan Naderi Mighan, Jelena Mišić, Vojislav B. Mišić
We provide a comprehensive analytical model for block distribution in the Ethereum distribution network. We begin with a model for node connectivity based on reported measurements' and model the distribution of blocks using a Jackson network together with a priority M/G/1 queuing system. Our results show that the probability distribution of block response time is close to exponential distribution, while block delivery time exhibits a hypo-exponential distribution. Both distributions exhibit a thicker tail than the exponential distribution due to the peculiarities of the gossip-like protocol used to propagate blocks and transactions in Ethereum.
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 Ethereum’s 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 ‘prefigurative imaginaries’ to describe how prefiguration works to create multiple, mutually entangled but distinct imaginaries.
Amir Mohammad Karimi Mamaghan, Amin Setayesh, Behnam Bahrak
Bitcoin and Ethereum are the two most used decentralized blockchains. These platforms use a notion of addresses to represent identities in the network. These addresses are publicly visible entities that tell where funds are sent and received on a blockchain. Each user can have multiple addresses in a cryptocurrency network. In this paper, we investigate the lifespan of addresses in these networks, in particular, the lifespan distribution and its relationship with the other features such as turnover, turnover in USD, and transaction count. Our results show that addresses' lifespans follow a Double Pareto-Lognormal (DPLN) distribution. We also showed the relationship between lifespan and turnover, turnover in USD, and transaction count follows a power-law distribution.