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.
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.
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.
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.
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.
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.
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.
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.
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.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
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.
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/}.
Yanan Gong, K. P. Chow, Siu Ming Yiu, Hing Fung Ting
Bitcoin is a widely used decentralized cryptocurrency. The proportion of Bitcoin transactions used for illegal activities is increasing. Mixing services are commonly applied to enhance anonymity and make transaction records more challenging to follow and analyze. The current research on peeling chains is generally based on heuristic algorithms to identify change addresses. However, due to the characteristics and limitations of the Bitcoin blockchain, there is no such ground truth to ensure the accuracy of each derived change address. This research analyzes the peeling chain patterns based on self-change addresses. The use of self-change addresses implies that the input address and the address used for receiving the change are controlled by the same entity. Also, each chain's transaction details and generated chain parameters are further verified for more precise results. Combining the two methods ensures the accuracy of the extracted peeling chains to some extent. And the corresponding behavior pattern of the extracted chains is studied.
Temporal Heterogeneous Networks (THNs) are evolving networks that characterize many real-world applications such as citation and events networks, recommender systems, and knowledge graphs. Forecasting THNs involves predicting future connections within a network that evolves over time and comprises diverse types of nodes and interactions with varying temporal dynamics. Although some Graph Neural Networks (GNNs) models have been successfully applied to forecast THNs, there is a lack of a general overview of how the message-passing computation could be extended to treat THNs. Moreover, most of the current solutions exhibit pitfalls in their training and evaluation strategies. Hence, in this work, we propose a graph deep learning framework for THN forecasting. Our framework decomposes the computation of a GNN layer into multiple components and introduces two different schemes to update embedding representations for THNs. This design allows the classification of existing solutions into special instances of our framework and highlights their potential limitations. We also extend the set of benchmarks for THNs by introducing two novel high-resolution temporal heterogeneous graph datasets derived from an emerging Web3 platform and a well-established e-commerce website. Overall, we conducted the first massive evaluation of THNs solutions over four temporal heterogeneous network datasets on two different future link prediction tasks using a fair newly introduced evaluation setting that considers the evolving nature of the data. Based on the limitations of existing solutions, we develop a new model that combines working techniques from previous models and leverages a new embedding update scheme. Experiments show the prediction power of our model compared to current solutions for link prediction in temporal graphs. Moreover, the experimental evaluation highlights the strengths and weaknesses of the different solutions and shows the effectiveness of our framework design.
Stefan Kambiz Behfar, Richard Mortier, Jon Crowcroft
The rapid growth of the Ethereum network necessitates advanced anomaly detection techniques to enhance security, transparency, and resilience against evolving malicious activities. While there have been significant strides in anomaly detection, they often fall short in capturing the intricate spatial-temporal patterns inherent in blockchain transactional data. This study presents a scalable framework that integrates Graph Convolutional Networks (GCNs) with Temporal Random Walks (TRW) specifically designed to adapt to the complexities and temporal dynamics of the Ethereum transaction network. Unlike traditional methods that focus on detecting specific attack types, such as front-running or flash loan exploits, our approach targets time-sensitive anomalies more broadly—detecting irregularities such as rapid transaction bursts, anomalous token swaps, and sudden volume spikes. This broader focus reduces reliance on pre-defined attack categories, making the method more adaptable to emerging and evolving malicious strategies. To ground our contributions, we establish three theoretical results: (1) the effectiveness of TRW in enhancing GCN-based anomaly detection by capturing temporal dependencies, (2) the identification of weight cancellation conditions in the anomaly detection process, and (3) the scalability and efficiency improvements of GCNs achieved through probabilistic sampling. Empirical evaluations demonstrate that the TRW-GCN framework outperforms state-of-the-art Temporal Graph Attention Networks (TGAT) in detecting time-sensitive anomalies. Furthermore, as part of our ablation study, we evaluated various anomaly detection techniques on the TRW-GCN embeddings and found that our proposed scoring classifier consistently achieves higher accuracy and precision compared to baseline methods such as Isolation Forest, One-Class SVM, and DBSCAN, thereby validating the robustness and adaptability of our framework.
Edgardo Brigatti, V. Rocha Grecco, Alexis Hernández, Mário Augusto Bertella
We introduce a general framework for empirically detecting interactions in communities of entities characterized by different features. This approach is inspired by ideas and methods coming from ecology and finance and is applied to a large dataset extracted from the cryptocurrency market. The inter-species interaction network is constructed using a similarity measure based on the log-growth rate of the capitalizations of the cryptocurrency market. The detected relevant interactions are only of the cooperative type, and the network presents a well-defined clustered structure, with two practically disjointed communities. The first one is made up of highly capitalized cryptocurrencies that are tightly connected, and the second one is made up of small-cap cryptocurrencies that are loosely linked. This approach based on the log-growth rate, instead of the conventional price returns, seems to enhance the discriminative potential of the network representation, highlighting a modular structure with compact communities and a rich hierarchy that can be ascribed to different functional groups. In fact, inside the community of the more capitalized coins, we can distinguish between clusters composed of some of the more popular first-generation cryptocurrencies, and clusters made up of second-generation cryptocurrencies. Alternatively, we construct the network of directed interactions by using the partial correlations of the log-growth rate. This network displays the important centrality of Bitcoin, discloses a core cluster containing a branch with the most capitalized first-generation cryptocurrencies, and emphasizes interesting correspondences between the detected direct pair interactions and specific features of the related currencies. As risk strongly depends on the interaction structure of the cryptocurrency system, these results can be useful for assisting in hedging risks. The inferred network topology suggests fewer probable widespread contagions. Moreover, as the riskier coins do not strongly interact with the others, it is more difficult that they can drive the market to more fragile states.
Hamzah M. Marhoon, Noorulden Basil, Alfian Ma’arif
Blockchain technology is relatively young but has the potential to disrupt several industries. Since the emergence of Bitcoin, also known as Blockchain 1.0, there has been significant interest in this technology. The introduction of Ethereum, or Blockchain 2.0, has expanded the types of data that can be stored on blockchain networks. The increasing popularity of blockchain technology has given rise to new challenges, such as user privacy and illicit financial activities, but has also facilitated technical advancements. Blockchain technology utilizes cryptographic hashes of user input to record transactions. The public availability of blockchain data presents a unique opportunity for academics to analyze it and gain a better understanding of the challenges in blockchain communications. Researchers have never had access to such an opportunity before. Therefore, it is crucial to highlight the research problems, accomplishments, and potential trends and challenges in blockchain network data analysis and communications. This article aims to examine and summarize the field of blockchain data analysis and communications. The review encompasses the fundamental data types, analytical techniques, architecture, and operations related to blockchain networks. Seven research challenges are addressed: entity recognition, privacy, risk analysis, network visualization, network structure, market impact, and transaction pattern recognition. The latter half of this section discusses future research directions, opportunities, and challenges based on previous research limitations.
Attention-Aware Social Choice tackles the fundamental conflict faced by some agent communities between their desire to include all members in the decision making processes and the limited time and attention that are at the disposal of the community members. Here, we investigate a combination of two techniques for attention-aware social choice, namely Natural Language Processing (NLP) and Sampling. Essentially, we propose a system in which each governance proposal to change the status quo is first sent to a trained NLP model that estimates the probability that the proposal would pass if all community members directly vote on it; then, based on such an estimation, a population sample of a certain size is being selected and the proposal is decided upon by taking the sample majority. We develop several concrete algorithms following the scheme described above and evaluate them using various data, including such from several Decentralized Autonomous Organizations (DAOs).
Methodologies to infer financial networks from the price series of speculative assets vary, however, they generally involve bivariate or multivariate predictive modelling to reveal causal and correlational structures within the time series data. The required model complexity intimately relates to the underlying market efficiency, where one expects a highly developed and efficient market to display very few simple relationships in price data. This has spurred research into the applications of complex nonlinear models for developed markets. However, it remains unclear if simple models can provide meaningful and insightful descriptions of the dependency and interconnectedness of the rapidly developed cryptocurrency market. Here we show that multivariate linear models can create informative cryptocurrency networks that reflect economic intuition, and demonstrate the importance of high-influence nodes. The resulting network confirms that node degree, a measure of influence, is significantly correlated to the market capitalisation of each coin ($ρ=0.193$). However, there remains a proportion of nodes whose influence extends beyond what their market capitalisation would imply. We demonstrate that simple linear model structure reveals an inherent complexity associated with the interconnected nature of the data, supporting the use of multivariate modelling to prevent surrogate effects and achieve accurate causal representation. In a reductive experiment we show that most of the network structure is contained within a small portion of the network, consistent with the Pareto principle, whereby a fraction of the inputs generates a large proportion of the effects. Our results demonstrate that simple multivariate models provide nontrivial information about cryptocurrency market dynamics, and that these dynamics largely depend upon a few key high-influence coins.
Abstract The growing attention on cryptocurrencies has led to increasing research on digital stock markets. Approaches and tools usually applied to characterize standard stocks have been applied to the digital ones. Among these tools is the identification of processes of market fluctuations. Being interesting stochastic processes, the usual statistical methods are appropriate tools for their reconstruction. There, besides chance, the description of a behavioural component shall be present whenever a deterministic pattern is ever found. Markov approaches are at the leading edge of this endeavour. In this paper, Markov chains of orders one to eight are considered as a way to forecast the dynamics of three major cryptocurrencies. It is accomplished using an empirical basis of intra-day returns. Besides forecasting, we investigate the existence of eventual long-memory components in each of those stochastic processes. Results show that predictions obtained from using the empirical probabilities are better than random choices.
The emergence of the Web3 paradigm has led to more and more systems built on blockchain technology and relying on cryptocurrency tokens – both fungible and non-fungible – to sustain themselves and generate profit. The growth and success of these platforms are strongly dependent on the growth and evolution of the trade relationships among users. In this context, it is of paramount importance to understand the mechanism behind the evolution and growth dynamics of these economic ties: however, in these systems the trade relationships are strictly intertwined with social dynamics, posing significant challenges in the analysis. One of the most important mechanisms behind the evolution of social networks is the triadic closure principle: given the strict link between social and economic spheres, the mechanism emerges as a potential candidate among mechanisms in literature. Therefore in this work, we extend the existing methodology for triadic closure studies and adapt it to directed networks. We performed an analysis centered around 3-node subgraphs known as “triads” and statistically significant triads referred to as “triadic motifs”, both from a static and temporal perspective. The methodology was applied to various decentralized socio-economic networks with distinct levels of social components. These networks include currency transfers from the blockchain-based online social media platform Steemit, trade relationships among NFT sellers and buyers on the Ethereum blockchain, and a blockchain-based currency designed for humanitarian aid called Sarafu. Our measurements show how triadic closure is relevant during the evolution of these platforms and, for a few aspects, more impactful than centralized online social networks, where triadic closure is also incentivized by recommendation systems. Moreover, we are able to highlight both similarities and differences across networks with different levels of social components, both from a static and temporal standpoint. Overall our work presents strong evidence that triadic closure is an important evolutionary mechanism in decentralized socio-economic networks. Our findings provide a stepping stone in the study of decentralized socio-economic networks. Understanding the evolution of other decentralized networks, not following the same Web3 paradigm or with different social components will provide valuable insight into the understanding of dynamics in decentralized systems and potentially improve their design process.
This paper addresses the lack of research on quantifying Maximal Extractable Value (MEV) on Ethereum Layer 2 networks (L2s). Our findings reveal a substantial amount of MEV to be extracted on L2s, particularly on Polygon, with a lower bound of $213 million surpassing previous estimates. We observe that the majority of detected MEV on L2s consists of arbitrage opportunities, as liquidations are rare. These results emphasize the need for continuous monitoring and analysis of MEV on L2s, promoting informed decision-making for network selection and highlighting the associated risks.