Arif Perdana, W. Eric Lee, Chu Yeong Lim, Gary Pan · 5 authors
The characteristics of cryptocurrencies, such as decentralization, fluctuation, and anonymity, have often raised ethical concerns about their impact on privacy, cybersecurity, prosperity, and liberty. With an increased awareness of the potential consequences, it is essential to address how, in the face of ethical challenges, and together with one’s unique cultural values, various influences may affect the issue of trust toward cryptocurrencies. From an inter-country perspective, this study examines how ethical elements and cultural dimensions can interact to influence trust. In particular, we examine the ethical and cultural aspects of trust formation among cryptocurrencies’ users in the three predominant countries of Germany, China, and the United States. We use configurational analysis to investigate the relationships among ethics, culture, and trust in cryptocurrencies across these countries. The results of this study contribute to a greater global understanding of how different configurations of ethics and culture can influence one’s trust in cryptocurrencies.
Blockchain technology, once limited to niche technological communities, has seen widespread global adoption in recent years, with the potential to reshape financial and social systems. Launched in July 2015, the Ethereum blockchain introduced programmable Smart Contracts. This innovation enabled the creation of user-defined crypto-assets adhering to the ERC-20 standard, supporting a wide range of decentralized applications beyond simple value transfer. We present a large-scale, temporally annotated dataset of ERC-20 token transactions recorded on the Ethereum blockchain. Spanning from November 2015 to December 2024, the dataset encapsulates the trading activity of 216,336,529 users trading 1,138,136 unique tokens, offering a detailed view of crypto-market activity over time. Uniquely, it enables the analysis of a financial ecosystem from its inception, providing rare insights into its structural evolution, participant dynamics, and emergent behaviors. As the largest publicly available resource of its kind, it supports research in blockchain analytics, market dynamics and temporal network analysis. The full dataset and accompanying code are released for public use.
This paper presents a framework for analyzing and modeling validator behavior in dynamic consensus protocols. A discrete state-based model is proposed in order to represent four key validator states: majority, non-faulty minority, faulty minority, and non-validator, enabling systematic behavioral analysis through three complementary metrics: Jensen-Shannon Divergence (JSD) for entropy-based behavioral differences, the Bhattacharyya Coefficient for distribution similarity, and Wasserstein distance for state transition costs. To identify coherent validator groups and detect outliers, an HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) clustering is used since it is well-suited for detecting clusters in data with varying densities. Using JSD-based similarity measures in HDBSCAN, transient convergence patterns and stable behavioral clusters are uncovered, even in decentralized networks with diverse fault conditions. Simulation results on a 50-node network demonstrate the framework’s effectiveness, providing insights into system dynamics and offering tools for validator selection, fault detection, and stability monitoring in distributed ledger systems. This approach is particularly relevant, as consensus protocols evolve beyond traditional PBFT (Practical Byzantine Fault Tolerance) implementations, combining theoretical metrics with clustering techniques to enhance consensus robustness.
Alessia Galdeman, Lucio La Cava, Matteo Zignani, Andrea Tagarelli · 5 authors
The rapid growth of Non-Fungible Tokens (NFTs) and the extensive trading activities associated with such an intriguing domain led to the emergence of large-scale and interconnected transaction networks involving the most prominent NFT markets. Despite such interdependencies representing an inestimable source of information for the proper understanding of the NFT landscape, previous studies treated each market separately, overlooking relevant phenomena. In this study, we explore a multilayer network modeling approach to analyze transactions in multiple NFT markets. We reveal previously unnoticed macroscopic and mesoscopic traits by investigating indicators that discern whether markets are independent or linked: users trading NFTs are organized in cross-market communities where multi-market users act as bridges across marketplaces, adapting to the diverse nature of the markets they operate in. We also conduct an in-depth examination of such multi-market users, studying their specific activity patterns that leave a distinctive mark on the system: the majority of multi-market users well differentiate their earnings and expenses among the markets, while a fraction of them is directed toward a more polarized money allocation based on the typology of the markets. By offering a fresh perspective on this intricate financial system and emphasizing the importance of perceiving the NFT markets as a unique and interconnected world, our study paves the way for further contributions aimed at unraveling the complexity of cryptosystems and understanding the latent phenomena across NFT markets.
Non-fungible tokens (NFTs) have emerged as a transformative innovation in art and technology, relying heavily on social networks for promotion and revenue generation. The value of NFTs is profoundly influenced by their scarcity, rarity, and unique breeding mechanisms, which present novel challenges for viral marketing strategies. In this paper, we introduce a new research problem of NFT Revenue Maximization (NRM), which focuses on maximizing revenue from the perspective of NFT marketplaces by optimally selecting users for viral marketing campaigns (NFT airdrops) and determining the ideal quantities of NFTs to release. We prove the hardness of NRM and propose an approximation algorithm named Quantity and Offspring-Oriented Airdrops (QOOA). Our algorithm leverages the concepts of Scarcity-Conscious Revenue and Valuation-based Quantity Inequality to prune suboptimal airdrops and quantities at an early stage. To further enhance revenue through NFT breeding, QOOA identifies and incentivizes Rare Trait Collectors to acquire multiple NFTs with rare traits, facilitating the breeding of high-value offspring. Experimental results demonstrate that QOOA significantly outperforms baselines, achieving up to 3.8 times higher revenue in large-scale social networks.
E. I. Agbedo, R. O. Osanakpa, Salami M. O, C. O. Kayoh · 5 authors
This study explores the intricate dynamics of digital asset engagement, employing a Markov chain model to examine peer-influenced adoption (θ) and event-triggered abandonment (γ) across diverse network structures. The study gives hindsight into mixing time (time to stationarity) analysis, which represents the duration required to achieve a stationary distribution, and investigates its upper bound along with a revised linear programming proof. Simulations reveal the significant impact of network architecture on the spread of adoption and abandonment behaviors over time. Random networks typically demonstrate faster mixing, facilitating rapid information dissemination and market stabilization. In contrast, structured networks like small-world and scale-free exhibit more complex and often slower mixing patterns, showing distinct vulnerabilities or resilience based on the prevailing dynamic. Phase diagrams outline areas of sustainable adoption, critical decline, and swift abandonment, showcasing the long-term viability of various digital asset categories (such as Bitcoin-like, Meme coin-like, and NFT-like) within these network landscapes. The research underscores the crucial influence of network structure on market efficiency, information flow, and the enduring sustainability of digital assets. Additionally, this study aims to provide practical insights for Web3 project teams striving to cultivate sustainable asset ecosystems.
Andrei-Theodor Ginavar, Alexandra Conda, Daniel Traian Pele, Miruna Mazurencu-Marinescu-Pele · 5 authors
Abstract This study examines the statistical characteristics of Bitcoin and the CRIX index through a dual analytical framework: Metcalfe’s network law and bubble dynamics via Log-Periodic Power Law (LPPL) modeling. The findings suggest that, over the medium to long term, Metcalfe’s law—which posits that a network’s value scales with the square of its user base—serves as a valid approach for assessing cryptocurrency value. However, its applicability to Bitcoin in the short term remains uncertain. To analyse price dynamics during speculative bubbles, the DS LPPLS method was employed, enabling the identification of bubble phases and the estimation of potential regime shifts. Ultimately, the research concludes that while Metcalfe’s law holds true over longer time horizons, its reliability in short-term scenarios and under varying data regimes is considerably questionable.
Abderahman Rejeb, Karim Rejeb, Heba F. Zaher, Steve Simske
This paper explores the intersection of blockchain technology and smart cities to support the transition toward decentralized, secure, and sustainable urban systems. Drawing on co-word analysis and BERTopic modeling applied to the literature published between 2016 and 2025, this study maps the thematic and technological evolution of blockchain in urban environments. The co-word analysis reveals blockchain’s foundational role in enabling secure and interoperable infrastructures, particularly through its integration with IoT, edge computing, and smart contracts. These systems underpin critical urban services such as transportation, healthcare, energy trading, and waste management by enhancing data privacy, authentication, and system resilience. The application of BERTopic modeling further uncovers a shift from general technological exploration to more specialized and sector-specific applications. These include real-time mobility systems, decentralized healthcare platforms, peer-to-peer energy exchanges, and blockchain-enabled drone coordination. The results demonstrate that blockchain increasingly supports cross-sectoral innovation, enabling transparency, trust, and circular flows in urban systems. Overall, the current study identifies blockchain as both a technological backbone and an ethical infrastructure for smart cities that supports secure, adaptive, and sustainable urban development.
The rapid development of Web3 has generated massive amounts of on-chain data, making it crucial to effectively analyze and understand the complex relationships within blockchain ecosystems. Although standard RAG techniques augment LLMs through external data retrieval, it falls short in capturing the intricate network of relationships in Web3 data. In this paper, this work introduces an innovative method that combines GraphRAG with community detection algorithms to analyze Web3 textual data. By constructing knowledge graphs from Web3-related documents and leveraging community structures, our system can better understand the semantic relationships and contextual connections in Web3 content, delivering higher-precision answers to domain-specific questions. Our experiments on real-world Web3 textual data show that our method achieves superior response accuracy and contextual understanding compared to traditional RAG approaches, especially for complex Web3 concepts and community-driven insights.
Purpose This study aims to examine Bitcoin’s demand and price dynamics as it transitions from a growth state to a mature state, focusing on user base expansion and inventory levels. It refines valuation models and financial strategies by analyzing Bitcoin’s shift from network-driven asset characteristics to commodity-like price behavior, offering insights for regulatory oversight. Design/methodology/approach Using the Pruned Exact Linear Time algorithm to identify regime shifts, instrumental variable (IV) regression models to address endogeneity and derivatives data to estimate convenience yield and implied volatilities, the study analyzes blockchain and market-level data from 2013 to 2020. Five hypotheses on Bitcoin’s demand, returns, inventory effects, convenience yield and implied volatility are tested. Findings In the growth state, Bitcoin demand rises with user base expansion, with 100 unique users increasing demand by 0.23%. In the mature state, inventory levels negatively impact returns, with a 133-bitcoin increase lowering returns by 1 basis point. Convenience yields decline with inventory, while implied volatility slopes increase, confirming Bitcoin’s commodity-like behavior. Research limitations/implications Findings rely on historical data and future research can explore similar patterns in other cryptocurrencies. Blockchain data limitations, such as address clustering and transaction anonymity, may impact results. Practical implications Results provide insights for traders, risk managers and policymakers. Portfolio managers can align investments with Bitcoin’s lifecycle, while derivative traders can leverage insights into convenience yields and implied volatility. Originality/value This study empirically tests Bitcoin’s transition from a growth-driven financial asset to a commodity-like asset. It integrates network effect and commodity pricing models, offering a unified framework for understanding Bitcoin’s lifecycle.
Phumudzo Lloyd Seabe, Claude Rodrigue Bambe Moutsinga, Edson Pindza
Abstract Predicting cryptocurrency prices is challenging due to market volatility and external influences like social media sentiment. This study integrates Twitter sentiment analysis with deep learning models (LSTM, GRU, Bi-LSTM, and Temporal Attention Model) to enhance Bitcoin price forecasting. Sentiment features were extracted using VADER and RoBERTa, with findings showing that RoBERTa-based models significantly outperform VADER. Bi-LSTM (RoBERTa) achieved the lowest MAPE of 2.01%, demonstrating the effectiveness of deep contextual embeddings. SHAP analysis identified Sentiment Momentum, RoBERTa Compound Score, and VADER Negativity Score as key predictors of price movements. These results highlight the value of sentiment-driven forecasting and provide insights for traders, investors, and researchers.
This exploratory study introduces a sentiment-based framework for the social dynamics of hype around emerging technologies to support strategic investment decisions and contribute to innovation and strategic management research. Drawing on theories of herding behaviour and information cascades, we analysed social media sentiment of the Reddit discourse and investigated bubbles in representative financial assets across four emerging technology cases ‘Metaverse’, ‘Decentralized Finance (DeFi)’, ‘Non-fungible Token (NFT)’, and ‘Hydrogen Economy’ over one-year periods. Additionally, we compared results against measures of search volumes, news coverage, patent filings, and academic publications. We find two essential characteristics of the social dynamics of hype: intensifying positive sentiment and increasing conformity of sentiment towards a positive majority. Since hype can distort decision-making and hinder objective innovation assessments, this study offers practitioners a sentiment-based approach to navigate speculative hype and support decision-making.
Η παρούσα διπλωματική εργασία εξετάζει τις επιθέσεις μηδενικής μεταφοράς στην αλυσίδα μπλοκ Ethereum, οι οποίες είναι μια μορφή απάτης, όπου οι επιτιθέμενοι εξαπατούν τους χρήστες, πείθοντάς τους να στείλουν χρήματα σε λάθος πορτοφόλι, στέλνοντας ψευδείς συναλλαγές μηδενικής αξίας. Ενώ η γενική ιδέα αυτών των επιθέσεων είναι γνωστή, δεν είναι ευρέως διαδεδομένη και δεν έχει γίνει αρκετή εκτενής έρευνα για να αποδειχθεί πόσο συχνά συμβαίνουν ή πώς να ανιχνεύονται αποτελεσματικά. Για να διορθώσουμε αυτό το κενό, κατασκευάσαμε ένα πλαίσιο Python χρησιμοποιώντας το Selenium για τη συλλογή δεδομένων πορτοφολιών απευθείας από το Etherscan. Συλλέξαμε περισσότερα από 18.000 πραγματικά ιστορικά πορτοφολιών χρηστών και φιλτραρίσαμε τις διευθύνσεις που ανήκουν σε ανταλλακτήρια ή μη πορτοφόλια. Στη συνέχεια, αναλύσαμε κάθε συναλλαγή για να εξαγάγουμε πληροφορίες όπως διευθύνσεις πορτοφολιών, χρονικές σφραγίδες, ποσά και αναγνωριστικά συναλλαγών. Σχεδιάσαμε δύο εκδοχές μεθόδων ανίχνευσης, μία χαλαρή και μία αυστηρή. Η χαλαρή εκδοχή εντόπισε περισσότερες πιθανές επιθέσεις, αλλά είχε περισσότερους ψευδώς θετικούς. Η αυστηρή εκδοχή πρόσθεσε ελέγχους για τους πρώτους και τελευταίους τέσσερις χαρακτήρες της διεύθυνσης του πορτοφολιού και σήμανε μόνο συναλλαγές κάτω από 2 δολάρια για να βελτιώσει την ακρίβεια. Στην αυστηρή εκδοχή, συνολικά, το 3,96% των πορτοφολιών είχαν γίνει στόχος, και ανιχνεύτηκαν περισσότερες από 11.600 επιθέσεις. Από αυτές, οι 1.205 ήταν επιτυχείς, αν και οι περισσότερες οδήγησαν μόνο σε μικρές απώλειες. Τα αποτελέσματα δείχνουν ότι, ενώ αυτές οι επιθέσεις δεν αποφέρουν μεγάλα κέρδη τις περισσότερες φορές, βασίζονται στον όγκο. Οι επιτιθέμενοι ελπίζουν ότι τελικά κάποιος θα κάνει ένα μεγάλο λάθος. Συνολικά, η μέθοδος ανίχνευσης λειτουργούσε καλά και μπορεί να αποτελέσει μια σταθερή βάση για την κατασκευή εργαλείων που θα βοηθήσουν τους χρήστες να παραμείνουν ασφαλείς.
Blockchain technology establishes trust among participants through technical means. However, some malicious nodes may compromise this trust through short-range reorganization attacks for their interest. This paper develops an agent-based model to systematically analyze Proof-of-Stake short-range reorganization attacks, where three types of agents interact through distributed consensus mechanisms with ex-ante, fine-grained, and ex-post reorganization attack strategies. Through rigorous simulation of agent decision-making dynamics, we identify that: (1) Compared with ex-ante reorganization, the ratio of malicious nodes required for ex-post reorganization is much larger. (2) Increasing the node number increases the difficulty of ex-ante and ex-post reorganization. (3) The number of nodes affects ex-post reorganization attacks more significantly than ex-ante attacks. (4) Fine-grained reorganization significantly reduces attack difficulty
This study introduces the Multilayer Token Network (MLTN), a mathematical framework for analyzing Ethereum token transfers while capturing inter-token transformations crucial to Decentralized Finance (DeFi). Focusing on prominent fund accounts, we propose the PageRank-CheiRank Trade Balance (PCTB), an econometric measure inspired by balance sheet principles to quantify trade behavior over time. Applying MLTN to 2018–2024 transaction data, we reveal Alameda Research’s evolving trade strategies, fund interdependencies, and token-specific accumulation and distribution patterns, offering new insights into on-chain financial activities.
Online social platforms for digital communication necessitate an in-depth understanding of their evolving dynamics, especially after the renewal requests brought about by new paradigms, such as Web3. The dynamics within online social networks (OSNs) are influenced by numerous factors, encompassing user behavior, content generation, platform features, and technological advancements, with triadic closure standing out as a prominent and influential element. In this study, we focus on the temporal aspects of triadic closure and its role in the evolution of OSNs, especially after the advent of the Web3 paradigm. By analyzing networks with timestamped links from diverse platforms based on different architectures, including communication, Web3-based, and trade networks, we developed a comprehensive analytical pipeline to support the study of triadic closure patterns. This pipeline includes an algorithm for the census of time-ordered triads, a vector-based model for representing growing networks (growth triadic profile), the identification of triadic closure rules (TERs), and the evaluation of the speed of the formation of closed triads. Our findings reveal significant variations in the impact of triadic closure across different OSNs, marked by diverse growth triadic profiles and varying formation speeds of closed triads as well as diversity in the predictability of evolutionary patterns based on triads. This study not only enhances the comprehension of triadic closure in the temporal evolution of OSNs but also provides valuable insights to be taken into account for the design and administration of online social platforms.
Abstract Decentralized and transparent nature of cryptocurrencies have lately increased investors interest in them. Forecasting cryptocurrency’s price accurately is crucial to come up with a good investment strategy, and such a forecast requires one to consider its unique attributes as well as high volatility. Even though many existing studies have focused on analyzing the cryptocurrency transaction graph topology, studies on the analysis of transaction graph’s impact on prices are quite limited. In this paper, we explore the forecasting ability of blockchain transaction graph-based attributes on Bitcoin’s and Ethereum’s future price via deep learning methods. More specifically, we came up with motif convolution module (MCM), a motif-based graph representation learning approach to take local structural knowledge into account more strongly in node and edge-attributed transaction graphs encoding substantial structural knowledge. Our proposed MCM constructs a motif dictionary without supervision, and employs a new motif convolution operation while extracting the vertices local structural context. Afterwards, we learn high-level vertex embeddings by using such structural context via multilayer perceptron and graph neural network. Overall, we extract the attributed transaction graphs temporally-evolving low-dimensional representations, and use such embedding data together with historical prices within self-attention-based LSTM to predict the future prices accurately. Our proposed approach outperforms all considered baselines in terms of both price and price direction prediction, showing the promise of efficient integration of transaction data into cryptocurrency price prediction.
Francesco Zola, Jon Ander Medina, A. Venturi, Raúl Orduna-Urrutia
Cryptocurrency users increasingly rely on obfuscation techniques such as mixers, swappers, and decentralised or no-KYC exchanges to protect their anonymity. However, at the same time, these services are exploited by criminals to conceal and launder illicit funds. Among obfuscation services, mixers remain one of the most challenging entities to tackle. This is because their owners are often unwilling to cooperate with Law Enforcement Agencies, and technically, they operate as 'black boxes'. To better understand their functionalities, this paper proposes an approach to analyse the operations of mixers by examining their address-transaction graphs and identifying topological similarities to uncover common patterns that can define the mixer's modus operandi. The approach utilises community detection algorithms to extract dense topological structures and clustering algorithms to group similar communities. The analysis is further enriched by incorporating data from external sources related to known Exchanges, in order to understand their role in mixer operations. The approach is applied to dissect the Blender.io mixer activities within the Bitcoin blockchain, revealing: i) consistent structural patterns across address-transaction graphs; ii) that Exchanges play a key role, following a well-established pattern, which raises several concerns about their AML/KYC policies. This paper represents an initial step toward dissecting and understanding the complex nature of mixer operations in cryptocurrency networks and extracting their modus operandi.
Erveton P. Pinto, Marcelo A. Pires, Rone N. da Silva, Sı́lvio M. Duarte Queirós
We report the first application of a tailored Complexity-Entropy Plane designed for binary sequences and structures. We do so by considering the daily up/down price fluctuations of the largest cryptocurrencies in terms of capitalization (stable-coins excluded) that are worth $circa \,\, 90 \%$ of the total crypto market capitalization. With that, we focus on the basic elements of price motion that compare with the random walk backbone features associated with mathematical properties of the Efficient Market Hypothesis. From the location of each crypto on the Binary Complexity-Plane (BiCEP) we define an inefficiency score, $\mathcal I$, and rank them accordingly. The results based on the BiCEP analysis, which we substantiate with statistical testing, indicate that only Shiba Inu (SHIB) is significantly inefficient, whereas the largest stake of crypto trading is reckoned to operate in close-to-efficient conditions. Generically, our $\mathcal I$-based ranking hints the design and consensus architecture of a crypto is at least as relevant to efficiency as the features that are usually taken into account in the appraisal of the efficiency of financial instruments, namely canonical fiat money. Lastly, this set of results supports the validity of the binary complexity analysis.
Machine learning (ML) based network attack traffic detection is an emerging security paradigm, which is capable of capturing various advanced network attacks according to the features of traffic. When leveraging such promising security application to protect P2P services, particularly distributed cryptocurrency systems, one detection model should be deployed on many nodes to handle various unseen traffic patterns generated by nodes around the world. However, unseen yet benign traffic patterns are commonly classified as attack traffic, and thus trigger massive false-positive (FP) alarms. Unfortunately, the common practice of retraining models to reduce FPs is not salable for large-scale P2P networks, which incurs prohibitive labor efforts of collecting traffic on each node individually. To effectively deploy ML based attack traffic detection systems to protect distributed networks, we present tNeuron that automatically identifies FPs triggered by unseen traffic via neuron activation pattern analysis, such that it significantly improves the performance on various nodes. Specifically, we construct a shadow model with Transformer encoders to extract the knowledge of traffic patterns. Afterward, we train a model that learns how to classify FPs among alarms raised by ML models according to neuron activation patterns of the shadow model. Our experiments on real Ethereum nodes show that tNeuron can reduce 83.40% FP for seven state-of-the-art ML based attack detection systems, when detecting 15 kinds of P2P network attacks, thereby significantly improving detection accuracy in nine different metrics. In addition, tNeuron is robust against various adversarial examples constructed by existing evasion attacks. Besides, it achieves real-time detection and is capable of handling massive FPs generated by many nodes in large-scale distributed networks.
Abstract During the last years, financial market contagion has become a critical concern for policymakers and investors, particularly with respect to the financial stability of cryptocurrency platforms. This paper explores the contagion effect among crypto exchanges employing the Susceptible–Infected–Recovered (SIR) model with time delay and investigates possible cooperative strategies. The SIR dynamical system is integrated with the replicator equation of evolutionary game theory to study the interplay between the spread of risk and the propensity of cryptocurrency platforms to become cooperative under the pressure of financial contagion. Different equilibrium points which correspond to both pure and mixed cooperative strategies characterize the resulting model. We carry out a theoretical analysis of the problem by studying the asymptotic behavior in the steady state. In addition, using extensive cryptocurrency market data from 2017 to 2023, we identify the key factors driving contagion and assess the dynamics of cooperative versus non-cooperative behavior. Our findings point out that cooperative strategies are essential to ensure financial stability, particularly in the long term, as they mitigate systemic risks and foster resilience. These results provide critical insights for policy makers and investors, offering actionable strategies to enhance the robustness of crypto markets and address the growing challenges of financial contagion in the digital asset ecosystem.