The traditional Power Law model for Bitcoin is limited; it struggles to simultaneously fit historical data points across different eras without piecewise parameter adjustments. Bitcoin's trajectory is more naturally described as a tangent-based hyper-exponential system, driven by absolute supply scarcity.
Q-PROOF is an experimental blockchain architecture based on adaptive consensus, aperiodic topology, and quadratic governance. The model integrates Aperiodic Consensus Relaxation (ACR), distributed reputation, Sybil attack defense, coordinated attack detection, and post-quantum migration pathways. This technical white paper outlines the core consensus engine, mathematical modeling of system tension, correlation-aware consensus mechanics, and benchmark comparisons demonstrating enhanced finality and resilience against coordinated network capture.
This paper examines Web3 ecosystems not merely as markets for digital assets, but as networked social spaces where economic transactions give rise to enduring social ties, shared narratives, and collective identities. Leveraging large-scale data mining of fused on-chain blockchain transactions and off-chain social media activity, we analyze over one hundred NFT collections to uncover how different forms of participation structure community formation in decentralized environments. Using network analysis, we identify distinct ecosystem roles, such as long-term holders, active traders, and short-term speculators, and demonstrate how each produces markedly different network topologies, levels of cohesion, and pathways for influence. We complement this structural analysis with discourse analysis of social media engagement, revealing how narrative production, visibility, and sustained interaction persist even as transactional activity declines. Our findings show that communities centered on holding behavior evolve from transactional networks into socially embedded ecosystems characterized by dense ties, decentralized influence, and ongoing cultural participation, while trader- and speculator-dominated networks remain fragmented and transactional. By linking network structure with discursive dynamics, this study provides a sociotechnical framework for understanding how value, identity, and inequality are negotiated in Web3 spaces. The approach offers a scalable method for detecting patterns of inclusion, exclusion, and representational imbalance, advancing network-based research on digital communities beyond purely economic or technical accounts.
This paper examines Web3 ecosystems not merely as markets for digital assets, but as networked social spaces where economic transactions give rise to enduring social ties, shared narratives, and collective identities. Leveraging large-scale data mining of fused on-chain blockchain transactions and off-chain social media activity, we analyze over one hundred NFT collections to uncover how different forms of participation structure community formation in decentralized environments. Using network analysis, we identify distinct ecosystem roles, such as long-term holders, active traders, and short-term speculators, and demonstrate how each produces markedly different network topologies, levels of cohesion, and pathways for influence. We complement this structural analysis with discourse analysis of social media engagement, revealing how narrative production, visibility, and sustained interaction persist even as transactional activity declines. Our findings show that communities centered on holding behavior evolve from transactional networks into socially embedded ecosystems characterized by dense ties, decentralized influence, and ongoing cultural participation, while trader- and speculator-dominated networks remain fragmented and transactional. By linking network structure with discursive dynamics, this study provides a sociotechnical framework for understanding how value, identity, and inequality are negotiated in Web3 spaces. The approach offers a scalable method for detecting patterns of inclusion, exclusion, and representational imbalance, advancing network-based research on digital communities beyond purely economic or technical accounts.
This paper extends the classical Avellaneda-Stoikov framework for optimal market making to blockchain networks with directed acyclic graph (DAG) structure. In DAG-based consensus protocols such as GHOSTDAG, multiple blocks are produced in parallel, creating a branching time structure that fundamentally alters the market maker's optimization problem. We derive a DAG-extended Hamilton-Jacobi-Bellman equation that incorporates the probability distribution over transaction acceptance, showing that optimal spreads depend on the anticipated ordering of parallel blocks. Our main theoretical result demonstrates that market makers achieve O(1/n) variance reduction in inventory risk by distributing quotes across n parallel execution paths, exploiting the transaction-level mutual exclusivity inherent to GHOSTDAG ordering. We extend the framework to K correlated assets (proving portfolio-level variance reduction of O(K/n)) and provide adversarial robustness analysis under bounded hash power attacks. Implementation analysis for the Kaspa network (10 BPS, k=124 post-Crescendo) addresses practical constraints including direct-to-miner submission requirements, fee incentive compatibility, and latency bounds. Monte Carlo simulations validate theoretical predictions, showing Sharpe ratio improvements of 40-82% over single-path strategies under realistic network conditions. This work establishes foundational theory for high-frequency decentralized finance applications on DAG-based blockchains.
Decentralized Finance (DeFi) has rapidly emerged as one of the most transformative applications of blockchain technology, enabling permissionless financial services including lending, borrowing, trading, and yield optimization through smart contract-based protocols. The complex interactions among users, protocols, and assets in the DeFi ecosystem naturally form intricate graph structures whose topology encodes critical information about system behavior, risk propagation, and economic vulnerabilities. This paper presents a comprehensive survey of graph-based analytical methods applied to the DeFi ecosystem. We systematically review transaction graphs, protocol dependency graphs, liquidity flow networks, and user interaction graphs, examining how graph-theoretic techniques-including community detection, centrality analysis, temporal graph mining, and anomaly detection-have been applied to understand DeFi dynamics. We further analyze how graph methods reveal systemic risks such as composability-induced contagion, flash loan attack patterns, and liquidity concentration. We propose a unified DeFi Graph Analysis Taxonomy (DGAT) that classifies existing work across graph construction methodologies, analytical objectives, and application domains. Comparative evaluation of current approaches identifies key research gaps and outlines directions for future investigation, particularly in cross-protocol risk modeling and real-time graph analytics for DeFi monitoring.
Wassim Sliti, Félix Cuadrado, Leandro Campos Hernáez, Juan C. Dueñas
Illicit activities and coordinated manipulations in the Non-Fungible Tokens (NFT) market remain significant concerns, driven by the pseudonymous and publicly transparent nature of blockchain transactions and the lack of market oversight. In this study, we introduce a novel framework for detecting suspicious behavior in NFT trading ecosystems through temporal graph analysis. Our approach represents the market as a large-scale, time-evolving transactional graph, capturing realistic market dynamics and the complex interactions between traders.By leveraging temporal graphs, we track not only connections between traders but also the evolution of these interactions over time, including their sequence, rhythm, and frequency, enabling the identification of anomalous behaviors that static graph representations cannot reveal and that may warrant further examination. Using an optimized temporal cycle-detection algorithm, we extract connected groups of wallets for in-depth behavioral analysis, uncovering patterns indicative of unusual coordinated manipulation. To overcome the absence of labeled ground-truth validation data, we employ a temporal motif–based validation, demonstrating that flagged entities exhibit trading behaviors significantly deviating from standard market dynamics. Our results highlight the potential of temporal graph–based methodologies to provide a scalable and effective risk-profiling and market-surveillance tool, assisting analysts and regulatory entities in narrowing the investigation space within the large, permissionless NFT markets and enhancing surveillance, risk detection, and regulatory oversight in decentralized NFT markets.
Baoyu Zhang, Tao Chen, Weishan Zhang, Tao Wang · 9 authors
In September 2024, Lebanon was rocked by an unprecedented cyber-physical attack using Pager bombs. The attack combined advanced cyber warfare techniques with physical destruction, resulting in significant loss of life, infrastructure damage, and geopolitical repercussions. In this paper, we analyze the attitudes on this attack, from both English and Arabic social media users, and investigate impacts on global electronic devices sales and usage. A new topic discovery approach using large models and small models collaboration is proposed. We compare English and Arabic topics generated on social media and find that people in different language spaces share common topics of anxiety on this event. By analyzing market share trends in both China and the United States, an obvious correlation can be found between this event and phone sales. In addition, we discuss the evolution of warfare, and how DAOs(Decentralized Autonomous Organizations) can be utilized to improve the security of electronic devices by secured monitoring of their whole lifecycle.
Decentralization has an important geographic dimension that conventional metrics, such as stake distribution, often overlook. Where validators operate affects resilience to regional shocks (e.g., outages, natural disasters, or government intervention) as well as fairness in reward access. Yet major blockchain protocols do not encode geographical location in their rules; instead, validator locations emerge from a combination of economic incentives, regulatory constraints, infrastructure availability, and validator deployment choices. When certain locations offer systematic advantages, validators may strategically co-locate to maximize expected rewards, as observed in Ethereum, where validators cluster along the Atlantic corridor, which exhibits favorable latency. In this paper, we propose a formal model of validators' geographical positioning incentives under Ethereum's protocol design, capturing the interaction between its two block-building paradigms, local and external block building, and the geographical distribution of validators and information sources. We analytically characterize the model under a mean-field approximation and complement this analysis with an agent-based simulation calibrated with real-world latency data to quantify how these incentives translate into geographical concentration under heterogeneous geographic and infrastructural conditions. Our results show that Ethereum's block-building architecture is not geographically neutral. Both paradigms generate location-dependent payoffs and incentives to relocate closer to payoff-relevant parties in order to reduce propagation delays, although through different underlying mechanisms. Asymmetric access to information sources further amplifies geographical centralization. We also demonstrate that consensus parameters, such as attestation thresholds and slot times, modulate latency sensitivity and can amplify these effects, acting as protocol-level levers. Finally, we discuss the implications of our findings for protocol design and outline potential mitigation directions informed by our analysis.
Abstract This study provides essential insights into how diffusion processes unfold in complex networks, with a focus on cryptocurrency blockchains and infrastructure networks. The structural properties of these networks, such as hub-dominated, heavy-tailed topology, network motifs, and node centrality, significantly influence diffusion speed and reach. Using epidemic diffusion models, specifically the Kertesz threshold model and the Susceptible-Infected (SI) model, we analyze key factors affecting diffusion dynamics. To assess the uncertainty in the fraction of infected nodes over time, we employ bootstrap confidence intervals, while Bayesian credible intervals are constructed to quantify parameter uncertainties in the SI models. Our findings reveal substantial variations across different network types, including Erdős-Rényi networks, Geometric Random Graphs, and Delaunay Triangulation networks, emphasizing the role of network architecture in failure propagation. We identify that network motifs are crucial in diffusion. We highlight that hub-dominated networks, which dominate blockchain ecosystems, provide resilience against random failures but remain vulnerable to targeted attacks, posing significant risks to network stability. Furthermore, centrality measures such as degree, betweenness, and clustering coefficient strongly influence the transmissibility of diffusion in both blockchain and critical infrastructure networks.
Marcin Wątorek, Marija Bezbradica, Martin Crane, Jarosław Kwapień · 5 authors
Based on the cryptocurrency market dynamics, this study presents a general methodology for analyzing evolving correlation structures in complex systems using the $q$-dependent detrended cross-correlation coefficient ρ(q,s). By extending traditional metrics, this approach captures correlations at varying fluctuation amplitudes and time scales. The method employs $q$-dependent minimum spanning trees ($q$MSTs) to visualize evolving network structures. Using minute-by-minute exchange rate data for 140 cryptocurrencies on Binance (Jan 2021-Oct 2024), a rolling window analysis reveals significant shifts in $q$MSTs, notably around April 2022 during the Terra/Luna crash. Initially centralized around Bitcoin (BTC), the network later decentralized, with Ethereum (ETH) and others gaining prominence. Spectral analysis confirms BTC's declining dominance and increased diversification among assets. A key finding is that medium-scale fluctuations exhibit stronger correlations than large-scale ones, with $q$MSTs based on the latter being more decentralized. Properly exploiting such facts may offer the possibility of a more flexible optimal portfolio construction. Distance metrics highlight that major disruptions amplify correlation differences, leading to fully decentralized structures during crashes. These results demonstrate $q$MSTs' effectiveness in uncovering fluctuation-dependent correlations, with potential applications beyond finance, including biology, social and other complex systems.
With the rise of cryptocurrencies, illicit activities such as money laundering, fraud, and Ponzi schemes have gained attention. Traditional methods using graph neural networks (GNNs) to detect illicit transactions treat the entire transaction network as input, which works well on small networks but struggles with large-scale blockchain data. To address this limitation, the authors propose a neighborhood subgraph-based method that combines GCN and LSTM. The GCN captures information from neighboring nodes for each transaction, enhancing the understanding of the network structure, while the LSTM tracks the sequence and variations of fund flows. Experimental results show that by using 3-hop neighborhood subgraphs, the method outperforms other baseline models while requiring data from only an average of 80 nodes, thereby significantly improving efficiency compared to methods that process the entire transaction network.
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.
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.
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.