Decentralization is a core principle of blockchain technology and Decentralized Autonomous Organizations (DAOs), enhancing security and resilience by distributing control across a network. Traditional metrics like the Gini coefficient and Nakamoto coefficient often fall short in capturing the complex dynamics of decentralization. This paper introduces the Apokedro decentralization index, a metric that evaluates decentralization by considering the probabilities of all possible subsets of nodes that could collectively centralize control. These concepts from game theory, such as the Nash equilibrium, and the Apokedro index, when incorporated, provide a nuanced assessment of centralization risks. Key contributions include the mathematical formulation of the index, an efficient computational algorithm utilizing pruning techniques, and benchmarking experiments that compare the index performance against traditional metrics across various statistical distributions. The Apokedro index offers a comprehensive tool for measuring decentralization in blockchain networks and DAOs.
Cryptocurrency network analysis consists of applying the tools and methods of social network analysis to transactional data issued from cryptocurrencies. The main difference with most online social networks is that users do not exchange textual content but instead value -- in systems designed mainly as cryptocurrency, such as Bitcoin -- or digital items and services in more permissive systems based on smart contracts such as Ethereum.
M. Venturini, Daniel García-Costa, Elena Álvarez-García, Francisco Grimaldo · 5 authors
Cryptocurrencies have recently been in the spotlight of public debate due to their embrace by the new US President, with crypto fans expecting a 'bull run'. The global cryptocurrency market capitalisation is more than \$3.50 trillion, with 1 Bitcoin exchanging for more than \$97,000 at the end of November 2024. Monitoring the evolution of these systems is key to understanding whether the popular perception of cryptocurrencies as a new, sustainable economic infrastructure is well-founded. In this paper, we have reconstructed the network structures and dynamics of Bitcoin from its launch in January 2009 to December 2023 and identified its key evolutionary phases. Our results show that network centralisation and wealth concentration increased from the very early years, following a richer-get-richer mechanism. This trend was endogenous to the system, beyond any subsequent institutional or exogenous influence. The evolution of Bitcoin is characterised by three periods, Exploration, Adaptation and Maturity, with substantial coherent network patterns. Our findings suggest that Bitcoin is a highly centralised structure, with high levels of wealth inequality and internally crystallised power dynamics, which may have negative implications for its long-term sustainability.
Abstract This proposal introduces a novel decision-making framework to advance safe economic activities in cyberspace. We focus on identifying anomalies within crypto-asset trading, recognized as potential sources of criminal activity, severely undermining the credibility of such assets. Detecting and mitigating such anomalies holds significant societal implications, particularly in fostering trust within blockchain networks. We aim to bolster the “social trust” inherent to blockchain technology by facilitating informed economic activities in cyberspace. To achieve this, we propose integrating two artificial intelligence (AI) systems into a blockchain-based decentralized autonomous organization (DAO). The first AI application involves amalgamating various anomaly indicators, spanning from cluster coefficient, entropy, triangular motif analysis, correlation tensor analysis, loop component by Hodge decomposition, loop causality detection, network classification using graph Laplacian, and persistent homology analysis, into a comprehensive indicator using a Boltzmann machine. The second AI application entails deploying conversational AI to guide and support traders, aiding them in making informed trading decisions. This system is designed to alert DAO members to anomalies based on the integrated indicators, especially during massive price fluctuations. We operate under the assumption of close collaboration between governments, experts, traders, system developers, and operators to effectively organize DAOs. The primary technical challenge in our proposal lies in developing a wallet assisted by an intelligent software agent capable of safe interactions with traders within a unified DAO. With this organization, we envision fostering a global economic ecosystem where physical and cyber worlds converge, allowing democratic economic participation.
Public blockchain has outstanding performance in transaction privacy protection because of its anonymity. The data openness brings feasibility to transaction behavior analysis. At present, the transaction data of the public chain are huge, including complex trading objects and relationships. It is difficult to extract attributes and predict transaction behavior by traditional methods. To solve the problems, we extract the transaction features to construct the Ethereum transaction heterogeneous information network (HIN), and propose graph-neural-network-based transaction prediction method for public blockchain in HINs, which can divide the network into subgraphs according to connectivity and make the prediction results of transaction behavior more accurate. Experiments show that the execution time consumption of the proposed transaction subgraph division method is reduced by 70.61% on average compared with the search method. The accuracy of the proposed behavior prediction method also improve compared with the traditional random walk method, with an average accuracy of 83.82%.
The Metaverse represents a collaborative virtual realm blending physical and digital realities, fostering limitless avenues for online interaction, discovery and innovation. As technological strides propel immersive virtual worlds to the forefront of social media platforms, scholarly interest in the Metaverse surges, prompting extensive discourse. Drawing from social identity theory, this article introduces a novel framework for analysing online polarisation within discussions on the Metaverse, specifically on X (Twitter). Leveraging a multifaceted approach that integrates clustering, social network analysis, and text mining, our study delves into both group and opinion polarisation dynamics surrounding the Metaverse. Our findings uncover distinct community divisions and network structures, shedding light on prevalent themes, such as ‘Non-Fungible Token (NFTs)’, ‘Virtual Products and Collections’, ‘Blockchain Technology’, ‘Gaming’, and ‘Financial Markets’ that resonate within the public discourse.
We apply network science methodologies to address analytical challenges in blockchain and Decentralized Finance (DeFi). The pseudonymous nature of Bitcoin and the complex, multi-token interactions of Ethereum-based protocols require tools that go beyond traditional blockchain analysis. We present three network-based frameworks for understanding actor behavior and financial activities in these decentralized systems. First, for Bitcoin, we introduce a money flow representation learning approach that encodes taint networks into graph embeddings to identify entities across multiple address clusters. Second, we analyze DeFi activity using ego network motif mining, which extracts recurring structures from token transfer networks. This method can infer transaction methods (e.g., deposits, swaps, borrowing) and characterizes user behavior, even when labels are incomplete or noisy. Third, we model multi-token interactions through a Multilayer Token Network that links cross-token flows. Using PageRank-CheiRank Trade Balance, we quantify accumulation versus dispersion strategies and uncover temporal shifts in trading behavior, illustrated through entities such as Alameda Research. Together, these frameworks show how network topology, motifs, and multilayer flows transform raw blockchain data into interpretable insights on identity, function, and financial strategy.
Money laundering using cryptocurrency poses significant threats to the blockchain ecosystem. Due to the decentralized and anonymous nature of cryptocurrencies, detecting such laundering activities is difficult. Although substantial research has been conducted, almost all existing methods detect cryptocurrency laundering from an individual perspective, ignoring the fact that money laundering is typically a group behavior. Group information should be very helpful in laundering behavior analysis, but such laundering groups are hard to be recognized due to anonymity and diversity of purposes of cryptocurrency transactions. To address this challenge, we design a multi-persona grouping algorithm that can effectively group accounts into persona subgraphs. Then, we extract two subgraph features: cycle basis number and cycle overlapping ratio, and build an unsupervised model to evaluate laundering scores of each subgraph. Extensive experiments on both synthetic and real-world datasets demonstrate that, compared with existing methods, our proposed method can improve detection accuracy by 17.4 percentage points on average. To the best of our knowledge, this is the first work on group-based detection of cryptocurrency laundering.
Jan 1, 2025·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Francesco Maria De Collibus, Carlo Campajola, Guido Caldarelli, Claudio J. Tessone
We explore patterns, regularities, and correlations in the evolving landscape of Ethereum-based tokens, both ERC-20 (fungible) and ERC-721 (non-fungible) to understand the factors contributing to the rise in certain tokens over others. By applying network science methodologies, minimum spanning trees, econometric autoregressive–moving-average (ARMA) models, and the study of accumulation processes, we are able to highlight a rising centralisation process. Not only do “rich” tokens get richer, but past transactions also emerge as more reliable predictors of new transactions. Our findings are validated across different samples of tokens.
Matteo Loporchio, Damiano Di Francesco Maesa, Anna Bernasconi, Laura Ricci
Abstract The increasing adoption of tokens on the Ethereum blockchain has given rise to many distinct economic communities whose activity history is publicly accessible. In this paper we study the communities of Ethereum fungible and non-fungible tokens, regulated, respectively, by the ERC-20 and ERC-721 standards. In particular, we focus on token transfers and consider the top 100 largest ERC-20 and ERC-721 ecosystems by number of transfers, modeling them as networks where nodes correspond to participants and edges represent token transfers. We analyze their main topological properties and conduct a clustering-based study to identify groups of graphs with similar topologies. Subsequently, we classify the networks based on the application domain of their corresponding token and investigate whether graphs with similar topologies correspond to tokens within the same domain. We also conduct a temporal analysis of token popularity based on the historical transfer activity. Our findings highlight the existence of common topological properties (e.g., absence of small world effect) across both types of tokens. In contrast, the clustering analysis indicates no evident connection between the token application domain and the structure of the induced transfer networks, with the exception of non-fungible tokens associated with spamming activities.
Marcin Wątorek, Marcin Królczyk, Jarosław Kwapień, Tomasz Stanisz · 5 authors
Multifractality is a concept that helps compactly grasping the most essential features of the financial dynamics. In its fully developed form, this concept applies to essentially all mature financial markets and even to more liquid cryptocurrencies traded on the centralized exchanges. A new element that adds complexity to cryptocurrency markets is the possibility of decentralized trading. Based on the extracted tick-by-tick transaction data from the Universal Router contract of the Uniswap decentralized exchange, from June 6, 2023, to June 30, 2024, the present study using Multifractal Detrended Fluctuation Analysis (MFDFA) shows that even though liquidity on these new exchanges is still much lower compared to centralized exchanges convincing traces of multifractality are already emerging on this new trading as well. The resulting multifractal spectra are however strongly left-side asymmetric which indicates that this multifractality comes primarily from large fluctuations and small ones are more of the uncorrelated noise type. What is particularly interesting here is the fact that multifractality is more developed for time series representing transaction volumes than rates of return. On the level of these larger events a trace of multifractal cross-correlations between the two characteristics is also observed.
This study presents a multifractal analysis of the Bitcoin price time series over the period of 2015 to 2024. The multifractal fluctuation analysis with detrending (MFDFA) method is widely used to study fractal properties in financial time series. The results of the MFDFA indicate that the multifractal spectrum of the Bitcoin price time series has a positive slope. The multifractal spectrum demonstrated greater volatility at small time intervals and more predictable behavior at large. The Hurst exponent, which is a measure of the long-term memory of the time series, is found to be 0.5191. This implies that the Bitcoin have weak autocorrelation and little tendency to trend. The results of the study provide new insights into the complexity of the Bitcoin market and contribute to the ongoing debate on the market efficiency of cryptocurrencies.
Blockchain technology, the foundation of cryptocurrencies like Bitcoin, has utility beyond finance due to its decentralized and secure transactional nature. However, today's blockchain networks face the challenge of low transaction throughput due to block propagation delays. In this study, we propose a novel approach that divides large blocks into segments and leverages multi-origin pipelining for segment dissemination, thereby expediting block propagation. The scheme also integrates the use of a Bloom filter for lightweight segment integrity verification to prevent the reception of tampered segments. Simulation results reveal that our scheme outperforms Bitcoin's legacy approach, demonstrating significant improvement in propagation speed, particularly for large blocks, while maintaining a low fork rate.