Blockchain Papers

Satpal Singh Kushwaha

No abstract is available for this record.

Joann Jasiak, Cheng Zhong

This paper examines and compares intraday and intraweek patterns in hourly and daily prices, returns, volumes and volatility of native cryptocurrencies, stablecoins and tokens traded on Bitstamp. We show that native cryptocurrencies and tokens share common intraday periodicity determined by the operating times of the NYSE, LSE and Hang Seng stock exchange markets. Periodic patterns are also documented in the returns on cryptocurrency market portfolio approximated by the PCA applied to intraday and intraweek cross-sectional correlation matrices of cryptocurrency returns. Stablecoins have distinct dynamics and their daily and hourly returns are uncorrelated with one another and with the returns on other cryptocurrencies. We introduce a functional CAPM to accommodate the periodic patterns and estimate it by regressing the functions of intraday and intraweek cryptocurrency returns on the market portfolio. We show that the return functions on Bitcoin, Ether, and Link satisfy affine relationships with the return functions of the market portfolio and their functional betas display periodic intraday and intraweek patterns. • Native cryptocurrency and tokens share common periodic patterns. • Stablecoins have distinct intraday and intraweek dynamics. • The returns on stablecoins are uncorrelated with other cryptocurrencies. • Tokens contribute more to the risk on cryptocurrency market than other coins. • The betas in functional CAPM of cryptocurrency are periodic functions.

Kin-Hon Ho, Yun Hou, Michael Georgiades, Ken C. K. Fong

The emerging cryptocurrency market is one of the largest financial markets in the world, with a market capitalization that is already surpassing the gross domestic product of many developed economies. Cryptocurrencies are increasingly being adopted as a means of transaction and ownership in the digital domain, particularly in areas like decentralized finance and non-fungible tokens. Known for its high volatility, this market offers investors the potential for higher returns than traditional financial markets like stocks, foreign exchange, and commodities. However, it remains underexplored in academic research. In this paper, we propose the use of social network analysis to effectively model and analyze the cryptocurrency market and conduct a comprehensive numerical study to explore its key properties, including correlation structure, topological characteristics, stability, and influence. Furthermore, we propose the use of centrality measures as novel indicators to improve the accuracy of cryptocurrency price movement predictions. Our research introduces a novel method for understanding and navigating the cryptocurrency market, enabling investors to integrate advanced analytical tools into their decision-making processes.

Joshua Eick

Cryptocurrency is starting to be considered as an asset class for investment portfolios because of the multiple competitive advantages it has and its beneficial correlation to other asset classes. Most investors in cryptocurrency are speculators driven by market sentiment, investing according to technical analysis. There is a gap between technical analysis and fundamental analysis in the area of cryptocurrency. With the adoption of fundamental analysis the real intrinsic value of cryptocurrency can be achieved with higher returns being gained. This research aims to identify key variables and valuation metrics of Ethereum Blockchain Networks in order to predict the intrinsic value of ether through linear multiple regression. This will involve presenting a model including fundamental variables of the Ethereum Blockchain Network and market sentiment with the objective of achieving higher returns for investors of ether. There will be a focus on fundamental analysis, rather than technical analysis, of cryptocurrency because it is presume that has a greater relation to the intrinsic value of cryptocurrency. Based on the research's unsupervised method of linear regression, a price prediction model of ether with a Mean Sum Square Error of 1.1266*e^-6 and R square of 99% is devised. The results indicate that the features of the Ethereum Blockchain Network and valuation metrics have more predicting power than the market sentiment (Crix-Crypto Index). The research highlight that the most significant variable to ether are gas price per block, transactions fees and reward to miners, and focused on the utility of ether which can be of intrinsic value and have a significant impact on investment portfolios.

Jung Lee, Han Woo Park

This study provides a pioneering analysis of the features and topics of news websites in the Web 3.0 era through a comparison with non-news sites. We first classify over 4,600 Web3 sites into news and non-news types to investigate the feature characteristics of decentralized websites as well as semantic and subject categories. The most novel finding is that Web3 news sites have more features than non-news sites in terms of design systems and functions, interactivity, information quality, and hyperlinks. Furthermore, news web3 domains have more terms in semantic networks associated with information provision (e.g., “source”), whereas non-news domains have more terms associated with finance (e.g., “token”). The integration of the Decentralized Autonomous Organization, a fundamental component of the Web3 ecosystem, began in the news industry and is at an early stage in terms of functionality and structure. The study’s results are discussed in the context of future Web3 domain development.

Giacomo Ibba, Sabrina Aufiero, Silvia Bartolucci, Rumyana Neykova · 7 authors

This paper presents MindTheDApp, a toolchain designed specifically for the structural analysis of Ethereum-based Decentralized Applications (DApps), with a distinct focus on a complex network-driven approach. Unlike existing tools, our toolchain combines the power of ANTLR4 and Abstract Syntax Tree (AST) traversal techniques to transform the architecture and interactions within smart contracts into a specialized bipartite graph. This enables advanced network analytics to highlight operational efficiencies within the DApp’s architecture. The bipartite graph generated by the proposed tool comprises two sets of nodes: one representing smart contracts, interfaces, and libraries, and the other including functions, events, and modifiers. Edges in the graph connect functions to smart contracts they interact with, offering a granular view of interdependencies and execution flow within the DApp. This network-centric approach allows researchers and practitioners to apply complex network theory in understanding the robustness, adaptability, and intricacies of decentralized systems. Our work contributes to the enhancement of security in smart contracts by allowing the visualisation of the network, and it provides a deep understanding of the architecture and operational logic within DApps. Given the growing importance of smart contracts in the blockchain ecosystem and the emerging application of complex network theory in technology, our toolchain offers a timely contribution to both academic research and practical applications in the field of blockchain technology.

Grande, Mar, F. Borondo, J. Borondo

Currently cryptocurrencies and Decentralized Finance (DeFi), which enable financial services on public blockchains, represents a new growing trend in finance. In contrast to financial markets, ruled by traditional corporations, DeFi is completely transparent as it keeps records of all transactions that occur in the network and makes them publicly available. The availability of the data represents an opportunity to analyze and understand the market from the complexity that emerges from the interactions of the actors (users, bots and companies) operating in the embedded market. In this paper we focus on the Ethereum network and our main goal is to show that the properties of the underlying transaction network provide further and useful information to forecast the evolution of the market. We aim to separate the non redundant effects of the blockchain transaction network properties from classic technical indicators and social media trends in the future price of Ethereum. To this end, we build two machine learning models to predict the future trend of the market. The first one serves as a base model and considers a set of the most relevant features according to the current scientific literature including technical indicators and social media trends. The second model considers the features of the base model, together with the network properties computed from the transaction networks. We found that the full model outperforms the base model and can anticipate 46 more rises in the price than the base model and 19 more falls.

Amit Kumar, Neha Sharma, Rahul Chauhan, Manish Sharma

The advent of Non-Fungible Tokens (NFTs) has brought about a paradigm shift with profound implications in the digital domain. The prevalence of Non-Fungible Tokens (NFTs) has led to a notable increase in lively and impactful discussions pertaining to them on the social media platform, Twitter. This study explores the realm of discourse surrounding non-fungible tokens (NFTs) on the social media platform Twitter, utilizing a comprehensive dataset obtained through the Twitter API. The scope of our research entails a thorough examination of the dissemination of tweets, levels of user interaction, geographical variation, and temporal patterns within this dynamic context. By employing rigorous techniques for data preparation, research, and visualization, we are able to reveal intricate patterns and trends. The examination of geographical patterns uncovers the extent of worldwide discussions surrounding non-fungible tokens (NFTs), whilst the analysis of time delves into the levels of interaction observed on an hourly, daily, and yearly basis. This study paper offers significant insights for stakeholders who aim to comprehend and leverage the influence of talks pertaining to non-fungible tokens (NFTs) on the Twitter platform. It presents practical implications for content providers, marketers, and researchers as they navigate the complex NFT ecosystem. This study sets the foundation for future inquiries into sentiment analysis, event-driven interaction, and the dynamic nature of debate surrounding digital assets in the evolving NFT arena.

Chenlei Liu, Yuhua Xu, Zhixin Sun

No abstract is available for this record.

Jiaxin Li, T Yu, Yannian Wang, Yue Sun

Bitcoin’ s anonymity greatly protects users’ privacy, but it also makes regulation difficult. In Bitcoin, a random number generates a public-private key pair, the public key generates an address, and the private key is used for digital signatures. Users can generate multiple pairs of public and private keys to trade with multiple bitcoin addresses. Discovering the relationships between these addresses and clustering the addresses of individual users helps infer the identity of the addresses. By analyzing the association of addresses in UTXO , it is found that multiple input addresses of a transaction are controlled by the same user, and thus the bitcoin addresses can be clustered. The transactions between the user data obtained after clustering are communality, so the Louvain algorithm is further used to analyze the relationship between users, the visual results are used to present the association between users, and the impact of the number of users on the algorithm results is analyzed. Finally, the Leiden algorithm proposed to solve the problem that Louvain algorithm may have poor connectivity or even disconnection between communities is used to discover the community of the clustered user data. Compare the results of Leiden algorithm and Louvain algorithm and analyze the difference between the two results.

Young-Hoon Park, Yejin Kim

Blockchain technologies have gained widespread use in security-sensitive applications due to their robust data protection. However, as blockchains are increasingly integrated into critical data management systems, they have become attractive targets for attackers. Among the various attacks on blockchain systems, distributed denial of service (DDoS) attacks are one of the most significant and potentially devastating. These attacks render the systems incapable of processing transactions, causing the blockchain to come to a halt. To address the challenge of detecting DDoS attacks on blockchains, existing visualization schemes have been developed. However, these schemes often fail to provide early DDoS detection since they typically display only past and current system status. In this paper, we present a novel visualization scheme that not only portrays past and current values but also forecasts future expected system statuses. We achieve these future predictions by utilizing polynomial regression with blockchain data. Additionally, we offer an alternative DDoS detection method employing statistical analysis, specifically the coefficient of determination, to enhance accuracy. Through our experiments, we demonstrate that our proposed scheme excels at predicting future blockchain statuses and anticipating DDoS attacks with minimal error. Our work empowers system managers of blockchain-based applications to identify and mitigate DDoS attacks at an earlier stage.

Arman Amiri, Madjid Tavana, Hosein Arman

No abstract is available for this record.

Kai Liu, Minghao Yu, Yang Jin, Yue Wang · 6 authors

The Web3 social platform Friend.Tech, launched in August 2023, enables users to tokenize and trade their social influence. While quickly attracted 139 thousand users in one month, Friend.Tech’s economic model and business strategies face significant challenges. After conducting a qualitative analysis of this platform, we collected and analyzed relevant on-chain data, and found that the platform’s economic model generates early substantial returns for key opinion leaders (KOLs) but also restricts community size and stable earning potential, with 99.4% of accounts having fewer than 100 followers. Numerous speculative users were attracted to the platform, but only 22.1% of speculative returns were positive, and the trading frequency rapidly declined, with the average token holding period exceeding four days. The reliance on new users, combined with an inevitable decline in platform activity, indicates a less optimistic outlook for the sustainability of this economic model. The platform demonstrates a prominent level of transitivity and tighter social connections compared to existing social platforms. In conclusion, while Friend.Tech appears economically unsustainable, its social model exhibits unique characteristics and serves as a remarkable exploration for the development of Web3.

Jack Nicholls, Aditya Kuppa, Nhien‐An Le‐Khac

Illicit activity in cryptocurrency has increased dramatically over the years. Bitcoin mechanics allow for users to mask their identity through obfuscation techniques. Much research has been published in the domain of identifying illicit activity in cryptocurrency, and in particular the emergence of Graph Neural Networks (GNNs) has shown great promise in this area. In this paper, we propose two graph preprocessing methods to improve performance and robustness of our node classification GNN models in identifying illicit transactions in the Bitcoin network. Our methods focus on graph restructuring through measuring the connectivity of nodes in a graph, and the similarity of the underlying features each node possesses. We demonstrate the graph restructuring methodologies on five GNN architectures and empirically show an improvement of evaluation metrics when compared against the unprocessed graph dataset. We compare our proposed methods against other imbalanced node classification techniques on a common graph dataset. This methodology has great opportunity in the transaction monitoring landscape for exchanges and financial institutions attempting to capture potential illicit activity taking place on their networks including money laundering.

Hanaa Abbas, Maurantonio Caprolu, Roberto Di Pietro

No abstract is available for this record.

Biresh Kumar, Sonali Singh, Pallab Banerjee, Mohan Kumar Dehury

Blockchain innovation has changed how monetary exchanges are directed, especially in the domain of digital forms of money. This paper dives into the many-sided universe of blockchain networks, offering progressed bits of knowledge into digital money exchange investigation and the hidden elements of these decentralized organizations. As of late, cryptographic forms of money have acquired huge prevalence, drawing in the two financial backers and scientists. To reveal insight into the inward functions of blockchain networks, we utilize a complex methodology that joins information investigation, network hypothesis, and cryptographic standards. Our exploration digs into different features of cryptographic money exchanges, including their namelessness, recognizability, and protection concerns. We investigate novel methods for de-anonymizing exchanges and following assets across the blockchain, revealing insight into the difficulties and valuable open doors for upgrading protection in this space. This incorporates an assessment of exchange affirmation times, network adaptability, and agreement systems. By acquiring a more profound comprehension of these elements, we mean to add to the continuous talk encompassing the versatility and security of blockchain networks.

Dan Lin, Jiajing Wu, Tao Huang, Kaixin Lin · 5 authors

To combat cybercrimes and maintain financial security for the blockchain ecosystem, “know your customer” (KYC) is an essential and also challenging process due to the pseudonymity nature of blockchain technology. To unlock the potential of KYC on blockchain-based platforms like Ethereum, account labeling is a powerful means which can de-anonymize addresses by mining public transaction records. Existing studies on account labeling are mainly conducted via machine learning (ML) methods fed with hand-crafted features or graph neural networks based on the modeled transaction network. However, ML approaches based on hand-crafted features ignore the global interaction information between accounts, making it easy for criminals to evade detection. Moreover, the performance of traditional GCN methods when applied to Ethereum transaction network encounters limitations due to label sparsity, network heterophily, and large network size of the transaction network. In this article, we first analyze Ethereum accounts involved in typical businesses, in terms of both account and topological features. Then based on the analytical results, we propose a novel GCN method named know-your-customer graph convolutional network (KYC-GCN) which contains two key designs: 1) multihop aggregators and importance-based sampling are designed to tackle the dilemma between accuracy and efficiency. 2) GCN architecture is improved to explicitly capture local and more global information. Experimental results on a realistic Ethereum dataset show that the proposed KYC-GCN (90.2% accuracy, 86.2% Marco-F1) achieves state-of-the-art classification performance, and results on six benchmarks demonstrate that it yields great performance under homophily and heterophily.

Dawei Xu, Jiaqi Gao, Liehuang Zhu, Feng Gao · 5 authors

Bitcoin is a decentralized digital cryptocurrency. Its network is a Peer-to-peer(P2P) network consisting of distributed nodes. Some of these nodes are always online and in this article are called Bitcoin backbone nodes. They have a significant impact on the stability and security of the Bitcoin network, so it is meaningful to analyze and discuss them. In this paper, we first continuously collect information about Bitcoin nodes from July 2021 through June 2022 (which is the longest duration of data collection to date). In total, we collect information on 127,613 Bitcoin nodes. At the same time, we conclude that the fluctuation of Bitcoin nodes is directly related to the fluctuation of onion network nodes. Further, we filtered 2694 Bitcoin backbone nodes based on our algorithm. By analyzing the backbone nodes' attributes such as geographic distribution, client version, operator, node function, and abnormal port number, it is demonstrated that these nodes are centralized and play an important role in the Bitcoin network. Based on this, three unsupervised machine learning algorithms are selected to cluster multiple attributes of backbone nodes in a more scientific way. In this paper, the whole process from data collection to cluster analysis is completed and the best results are obtained by comparison. The experiments proved the existence of centralization of Bitcoin backbone nodes and obtained the number of nodes within each cluster. Finally, cluster nodes are de-anonymized based on the optimal results. Through our experiments, we obtain organizational information about the deployers of 103 nodes, linking the Bitcoin backbone nodes to the real world, thus accurately demonstrating the existence of Bitcoin centrality.

Florian Spychiger, Michael Lustenberger, Jens Martignoni, L. Schädler · 5 authors

Blockchain and its related concept of decentral autonomous organization (DAO) is starting to influence project management. But how might project management supported by blockchain technology look like? And how would such a new form change and affect traditional project management? Not many concepts have been designed or even implemented yet. We chose an experimental framework to answer the first aspects of these questions. We developed a Decentralized Autonomous Project Organization (DAPO) and conducted an experiment to study the impact of blockchain on traditional project management. We show that such a blockchain-based approach can support the management of simple projects. Further, a fair and clear incentive scheme seems crucial and influences the way team members engage in the work. Also, more decentralized project management increases the importance of social aspects-related project management principles such as teamwork, self-organization, and cultural aspects, while principles related to budget, objectives, and schedule remain unchanged.

Stefan Kambiz Behfar, Richard Mortier, Jon Crowcroft

Blockchain technology has revolutionized the way information is propagated in decentralized networks. Ethereum plays a pivotal role in facilitating smart contracts and decentralized applications. Understanding information propagation dynamics in Ethereum is crucial for ensuring network efficiency, security, and scalability. In this study, we propose an innovative approach that utilizes Graph Convolutional Networks (GCNs) to analyze the information propagation patterns in the Ethereum network. The first phase of our research involves data collection from the Ethereum blockchain, consisting of blocks, transactions, and node degrees. We construct a transaction graph representation using adjacency matrices to capture the node embeddings; while our major contribution is to develop a combined Graph Attention Network (GAT) and Reinforcement Learning (RL) model to optimize the network efficiency and scalability. It learns the best actions to take in various network states, ultimately leading to improved network efficiency, throughput, and optimize gas limits for block processing. In the experimental evaluation, we analyze the performance of our model on a large-scale Ethereum dataset. We investigate effectively aggregating information from neighboring nodes capturing graph structure and updating node embeddings using GCN with the objective of transaction pattern prediction, accounting for varying network loads and number of blocks. Not only we design a gas limit optimization model and provide the algorithm, but also to address scalability, we demonstrate the use and implementation of sparse matrices in GraphConv, GraphSAGE, and GAT. The results indicate that our designed GAT-RL model achieves superior results compared to other GCN models in terms of performance. It effectively propagates information across the network, optimizing gas limits for block processing and improving network efficiency.

Zhen Zhang, Bingqiao Luo, Shengliang Lu, Bingsheng He

Numerous studies have been conducted to investigate the properties of large-scale temporal graphs. Despite the ubiquity of these graphs in real-world scenarios, it's usually impractical for us to obtain the whole real-time graphs due to privacy concerns and technical limitations. In this paper, we introduce the concept of {\it Live Graph Lab} for temporal graphs, which enables open, dynamic and real transaction graphs from blockchains. Among them, Non-fungible tokens (NFTs) have become one of the most prominent parts of blockchain over the past several years. With more than \$40 billion market capitalization, this decentralized ecosystem produces massive, anonymous and real transaction activities, which naturally forms a complicated transaction network. However, there is limited understanding about the characteristics of this emerging NFT ecosystem from a temporal graph analysis perspective. To mitigate this gap, we instantiate a live graph with NFT transaction network and investigate its dynamics to provide new observations and insights. Specifically, through downloading and parsing the NFT transaction activities, we obtain a temporal graph with more than 4.5 million nodes and 124 million edges. Then, a series of measurements are presented to understand the properties of the NFT ecosystem. Through comparisons with social, citation, and web networks, our analyses give intriguing findings and point out potential directions for future exploration. Finally, we also study machine learning models in this live graph to enrich the current datasets and provide new opportunities for the graph community. The source codes and dataset are available at https://livegraphlab.github.io.

Xiaohan Li, Yanbo Yang, Baoshan Li, Minchao Li · 6 authors

As cryptocurrency is widely used in the financial field, detecting anomalous trading behavior of blockchain-based cryptocurrencies has become increasingly important. Researchers have utilized graph convolutional neural networks (GCN) for detecting anomalous cryptocurrency transactions. However, GCN fails to fully capture the spatial information correlation between neighboring nodes when processing graph data, which limits the utilization of structural features in the model. Therefore, the performance of GCN may be constrained when dealing with complex, high-dimensional graph data. In this paper, we propose a cosine similarity-based graph convolutional neural network for detecting anomalous cryptocurrency transactions. Compared to traditional GCN models, our method can better utilize both the network structure features and spatial information correlation, and it performs better in processing infinite-dimensional graph data. Experimental results show that our model can effectively detect anomalous cryptocurrency transactions, thereby improving the security and reliability of cryptocurrency, and it has good application prospects.

Noor Al-Gburi, Attila Klenik, Imre Kocsis

Visual Exploratory Data Analysis (EDA) is a key step in data analysis – however, after decades of research, recommending visualizations and their sequences for a human analyst in an exploration-supporting, efficient and repeatable way is still not a solved problem. However, EDA in the empirical assessment of performance and dependability of complex IT systems has key differences from the general setting: system structure and behavior have at least partial specifications, and the EDA process tends to follow established engineering processes (e.g., for diagnosis). Utilizing these differences, in this paper, we propose a novel, semantically driven approach for rapidly setting up analytic notebooks for the IT performance and dependability EDA of complex systems. An ontology-based knowledge base connects observed and inferred operational data with operational semantics and deployment topology; rule-based inference on the knowledge base creates a model of EDA notebook structure and plot recommendations. The model is automatically translated to notebook code and connected to the input data. We also present an open, end-to-end proof of concept implementation of the approach for the transaction duration analysis of Hyper-ledger Fabric, a complex, cross-organizational distributed ledger platform.

Qian Wang, Zhang Zhen, Zemin Liu, Shengliang Lu · 6 authors

While numerous public blockchain datasets are available, their utility is constrained by an exclusive focus on blockchain data. This constraint limits the incorporation of relevant social network data into blockchain analysis, thereby diminishing the breadth and depth of insight that can be derived. To address the above limitation, we introduce EX-Graph, a novel dataset that authentically links Ethereum and X, marking the first and largest dataset of its kind. EX-Graph combines Ethereum transaction records (2 million nodes and 30 million edges) and X following data (1 million nodes and 3 million edges), bonding 30,667 Ethereum addresses with verified X accounts sourced from OpenSea. Detailed statistical analysis on EX-Graph highlights the structural differences between X-matched and non-X-matched Ethereum addresses. Extensive experiments, including Ethereum link prediction, wash-trading Ethereum addresses detection, and X-Ethereum matching link prediction, emphasize the significant role of X data in enhancing Ethereum analysis. EX-Graph is available at \url{https://exgraph.deno.dev/}.