Vojislav B. Mišić, Soosan Naderi Mighan, Jelena Mišić, Xiaolin Chang
Proof-of-Stake (PoS) protocols are widely accepted as a viable substitute for the Proof-of-Work-based consensus, which is why recent blockchain-based cryptocurrencies and applications, most notably Ethereum 2.0, are using some variant of PoS as the basis for the consensus protocol. However, the implementation of PoS protocols in Ethereum 2.0 are not without its share of problems and vulnerabilities, especially with respect to the malicious behavior of validator nodes. In this paper, we first review the basic tenets of PoS protocols. We then discuss some of the recently described attacks on the Ethereum 2.0 consensus, and we also show that some of the design rationales adopted in PoS implementation—the decentralization of the voting process in particular—have, in actuality, enabled attacks that can be launched at a very low cost to the attacker. We also propose simple remedies that can reduce or eliminate the impact of those attacks and can evaluate the performance of the Ethereum 2.0 consensus when these remedies are applied.
Cryptocurrency and blockchain technology have changed the financial environment, giving decentralized and secure alternatives to existing institutions. Cryptocurrencies, like Bitcoin and Ethereum, leverage blockchain as a distributed ledger to record transactions openly and immutably. The blockchain's decentralized structure maintains trust and removes the need for middlemen, boosting efficiency and decreasing fraud risks. Cryptocurrencies allow borderless and fast transactions, encouraging financial inclusion internationally. Despite their transformational potential, problems such as regulatory uncertainty and environmental concerns surround cryptocurrencies. The continued advancement of this revolutionary technology has spurred questions about its long-term implications on finance, government, and beyond. As the world continues to explore the huge potential of cryptocurrencies and blockchain, their disruptive effect on numerous sectors remains a major topic of attention and controversy.
Ahmed Akhtar, Masoud Barati, Basit Shafiq, Omer Rana · 7 authors
The use of blockchain technology has been proposed to provide auditable access control for individual resources. Unlike the case where all resources are owned by a single organization, this work focuses on distributed applications such as business processes and distributed workflows. These applications are often composed of multiple resources/services that are subject to the security and access control policies of different organizational domains. Here, blockchains provide an attractive decentralized solution to provide auditability. However, the underlying access control policies may have event-driven constraints and can be overlapping in terms of the component conditions/rules as well as events. Existing work cannot handle event-driven constraints and does not sufficiently account for overlaps leading to significant overhead in terms of cost and computation time for evaluating authorizations over the blockchain. In this work, we propose an automata-theoretic approach for generating a cost-efficient composite access control policy. We reduce this composite policy generation problem to the standard weighted set cover problem. We show that the composite policy correctly captures all the local access control policies and reduces the policy evaluation cost over the blockchain. We have implemented the initial prototype of our approach using Ethereum as the underlying blockchain and empirically validated the effectiveness and efficiency of our approach. Ablation studies were conducted to determine the impact of changes in individual service policies on the overall cost.
At present, there are approximately 9300 types of cryptocurrencies in the cryptocurrency market, with Bitcoin, Ethereum, and other representative cryptocurrencies receiving widespread attention. Currently, scholars have conducted a lot of research on stock pricing using the Capital Asset Pricing Model (CAPM), but there is little research on the application of CAPM in the emerging cryptocurrency market. This article uses the CAPM to conduct an applicability analysis of CAPM of the top 10 cryptocurrencies in the cryptocurrency market from August 1 2021 to July 31 2023. The analysis process involves first conducting a mathematical analysis of the returns and coefficient of cryptocurrencies, and then using the OLS regression model to fit the cryptocurrency data and CAPM formula with analyses of the OLS regression results. The research conclusion of this article is that in most cases, CAPM is not applicable in the cryptocurrency market. This article demonstrates an applicability analysis of CAPM in the cryptocurrency field, providing reference value for scholars conducting research in this field and investors in cryptocurrencies.
Dmitry Mikhaylov, Abdulla Shafeeg, Ilman Shazhaev, Arbi Tularov
NFTs are non-fungible tokens. Like cryptocurrencies, they appeared as a result of the formation of the blockchain. True, unlike, say, Bitcoin, each NFT is unique and has its own value. If one conventional cryptocurrency unit does not differ in any way from another similar one (for example, one Ethereum does not differ in any way from another and is completely interchangeable without loss of value), then the situation is different with NFT. Tokens are used to secure rights to a unique object, such as a digitized work of art or artifacts in video games. Tokens are stored in the blockchain and contain the address of the object. To store the metadata and media to which the tokens are assigned, the InterPlanetary File System (IPFS) file system distributed over many computers is most often used. Farcana has suggested applying the same principle to scientific research. By introducing the MetaDeSci, Farcana has created a space where the gamers, gaming platforms, investors, scientists and universities are capable of using the rNFT to finance research, share data and promote science altogether, maintaining transparency.
Blockchain technology, originating with Nakamoto's seminal whitepaper in 2008, has evolved into a transformative force, promising decentralized, transparent, and secure digital transactions. However, the burgeoning impact of blockchain is accompanied by inherent scalability challenges that impede its widespread adoption and efficiency. This study conducts a comparative analysis of scalability challenges within prominent blockchain networks Bitcoin, Ethereum, and Binance Smart Chain. Rooted in a holistic understanding of blockchain evolution, the analysis focuses on transaction throughput, latency, consensus mechanisms, network congestion, and resource utilization. Consensus mechanisms, serving as the bedrock of blockchain networks, play a pivotal role in scalability dynamics. The study dissects the implications of diverse mechanisms Proof of Work (PoW), Proof-of-Stake (PoS), and Delegated Proof-of-Stake (DPoS) to unravel their impact on transaction efficiency. Network congestion emerges as a critical hurdle, affecting transaction confirmation times and overall system efficiency. Balancing this congestion requires a nuanced understanding of network dynamics, block sizes, and transaction prioritization. Resource utilization, particularly in terms of computing power and memory, becomes a focal point for sustainable growth. The study scrutinizes the efficiency of resource allocation and its implications for scalability, acknowledging the delicate balance between network performance and ecological considerations. By juxtaposing these blockchain networks, the comparative analysis aims to discern patterns, evaluate the effectiveness of consensus mechanisms, and provide actionable insights to address scalability challenges. Ethical considerations underpin the study, ensuring responsible data usage, user privacy, and adherence to ethical standards in blockchain research. This exploration into scalability challenges within blockchain networks contributes to the broader discourse on decentralized technologies. The findings are poised to inform future developments, providing a roadmap for enhancing scalability while maintaining the core tenets of decentralization, security, and sustainability.
The purpose of this study is to test the ability of the Autoregressive Integrated Moving Average (ARIMA) model to predict the Ethereum value which fluctuates greatly due to the Rusian and Ukraine War. The population in this study is daily closing price data for the period May 2021 to May 2022, so the sample in this study is 396 data time series data. The results showed that the best ARIMA model for predicting the Ethereum value was ARIMA (1,1,0). ARIMA (1,1,0) can predict the Ethereum value pretty good because the value of the forecasting results is not much different from the actual value. This is also evidenced by the results of the accuracy test using MAPE which has a result of 0,448 which means the accuracy of forecasting is 55,2%.
In the evolving landscape of Internet technologies, where decentralized systems, especially blockchain-based computation and storage like Ethereum Virtual Machine (EVM), Arweave, and IPFS, are gaining prominence, there remains a stark absence of a holistic decentralized communication framework. This gap underlines the pressing necessity for a protocol that not only enables seamless cross-platform messaging but also allows direct messaging to wallet addresses, fostering interoperability and privacy across diverse platforms. SendingNetwork addresses this need by creating a reliable and secure decentralized communication network, targeting essential challenges like privacy protection, scalability, efficiency, and composability. Central to our approach is the incorporation of edge computing to form an adaptive relay network with the modular libp2p library. We introduce a dynamic group chat encryption mechanism based on the Double Ratchet algorithm for secure communication and propose a Delegation scheme for efficient message processing in large group chats, enhancing both resilience and scalability. Our theoretical analyses affirm the Delegation scheme's superior performance. To bolster system stability and encourage node participation, we integrate two innovative consensus mechanisms: "Proof of Relay" for validating message relay workload based on the novel KZG commitment, and "Proof of Availability" for ensuring network consistency and managing incentives through Verkle trees. Our whitepaper details the network's key components and architecture, concluding with a roadmap and a preview of future enhancements to SendingNetwork.
Abstract The facilitation of sharing and exchanging patients’ health records is a paramount opportunity in e-health, enabling healthcare providers to garner a comprehensive and clear perspective of patients’ medical histories without necessitating direct inquiries. Besides this great advantage, it introduces substantial issues on security and privacy, mainly related to unauthorized access to e-health records when different healthcare service providers maintain records. In this paper, we deal with this problem and propose using the blockchain technology (1) to obfuscate the linkage between patients’ identities and their e-health records and (2) to grant access to e-health records exclusively to entities authorized by patients themselves. Key outcomes include using a digital identity based on the Electronic Identification, Authentication, and Trust Services Regulation (eIDAS) to control access to these records, and a concrete implementation by adopting the Ethereum blockchain. Our solution relies on using a public blockchain, which is an improvement for the state of the art, in which only private or consortium blockchains have been proposed. The resulting solution has been analyzed, and the effectiveness and affordability of the proposal have been shown.
We study a new "laminated" queueing model for orders on batched trading venues such as decentralised exchanges. The model aims to capture and generalise transaction queueing infrastructure that has arisen to organise MEV activity on public blockchains such as Ethereum, providing convenient channels for sophisticated agents to extract value by acting on end-user order flow by performing arbitrage and related HFT activities. In our model, market orders are interspersed with orders created by arbitrageurs that under idealised conditions reset the marginal price to a global equilibrium between each trade, improving predictability of execution for liquidity traders. If an arbitrageur has a chance to land multiple opportunities in a row, he may attempt to manipulate the execution price of the intervening market order by a probabilistic blind sandwiching strategy. To study how bad this manipulation can get, we introduce and bound a price manipulation coefficient that measures the deviation from global equilibrium of local pricing quoted by a rational arbitrageur. We exhibit cases in which this coefficient is well approximated by a "zeta value' with interpretable and empirically measurable parameters.
Mario Iván Contreras-Valdez, Sonal Sahu, José Antonio Núñez Mora, Roberto J. Santillán‐Salgado
In the broader landscape of cryptocurrency risk management, this study delves into the nuanced estimation of Value-at-Risk (VaR) for a uniformly weighted portfolio of cryptocurrencies, employing the bivariate Normal Inverse Gaussian distribution renowned for its semi-heavy tails. Utilizing high-frequency data spanning between 1 January 2017 and 25 October 2022, with a primary focus on Bitcoin and Ethereum, our research seeks to accentuate the resilience of VaR methodology as a paramount risk assessment tool. The essence of our investigation lies in advancing the comprehension of VaR accuracy by quantitatively comparing the observed returns of both cryptocurrencies with their corresponding estimated values, with a central theme being the endorsement of the Normal Inverse Gaussian distribution as a potent model for risk measurement, particularly in the domain of high-frequency data. To bolster the statistical reliability of our results, we adopt a forward test methodology, showcasing not only a contribution to the evolution of risk assessment techniques in Finance but also underscoring the practicality of sophisticated distributional models in econometrics. Our findings not only contribute to the refinement of risk assessment methods but also highlight the applicability of such models in precisely modeling and forecasting financial risk within the dynamic realm of cryptocurrencies, epitomized by the case study of Bitcoin and Ethereum.
<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">This paper examines the efficiency, in its weak form, of the clean energy stock indices, Clean Coal Technologies, Clean Energy Fuels, and Wilderhill, as well as the cryptocurrencies classified as "dirty", due to their excessive energy consumption, such as Bitcoin (BTC), Ethereum (ETH), Ethereum Classic (ETH Classic), and Litecoin (LTC), from January 2020 to May 30, 2023. In order to meet the research objectives, the aim is to answer the following research question, namely whether: i) the events of 2020 and 2022 accentuated the persistence in the clean energy and dirty energy indices? The results show that clean energy indices such as digital currencies classified as "dirty" show autocorrelation in their returns; the prices are not independent and identically distributed (i.i.d). In conclusion, arbitrage strategies can be used to obtain abnormal returns, but caution is needed as prices can rise above their real market value and reduce trading profitability. This study contributes to the knowledge base on sustainable finance by teaching investors how to use forecasting strategies on the future values of their investments.</span></p>
The research delves into the capabilities of a transformer-based neural network for Ethereum cryptocurrency price forecasting. The experiment runs around the hypothesis that cryptocurrency prices are strongly correlated with other cryptocurrencies and the sentiments around the cryptocurrency. The model employs a transformer architecture for several setups from single-feature scenarios to complex configurations incorporating volume, sentiment, and correlated cryptocurrency prices. Despite a smaller dataset and less complex architecture, the transformer model surpasses ANN and MLP counterparts on some parameters. The conclusion presents a hypothesis on the illusion of causality in cryptocurrency price movements driven by sentiments.
Zahra Ahani, Moein Shahiki Tash, Yoel Ledo Mezquita, Jason Angel
This paper provides an extensive examination of a sizable dataset of English tweets focusing on nine widely recognized cryptocurrencies, specifically Cardano, Binance, Bitcoin, Dogecoin, Ethereum, Fantom, Matic, Shiba, and Ripple. Our primary objective was to conduct a psycholinguistic and emotion analysis of social media content associated with these cryptocurrencies. To enable investigators to make more informed decisions. The study involved comparing linguistic characteristics across the diverse digital coins, shedding light on the distinctive linguistic patterns that emerge within each coin's community. To achieve this, we utilized advanced text analysis techniques. Additionally, our work unveiled an intriguing Understanding of the interplay between these digital assets within the cryptocurrency community. By examining which coin pairs are mentioned together most frequently in the dataset, we established correlations between different cryptocurrencies. To ensure the reliability of our findings, we initially gathered a total of 832,559 tweets from Twitter. These tweets underwent a rigorous preprocessing stage, resulting in a refined dataset of 115,899 tweets that were used for our analysis. Overall, our research offers valuable Perception into the linguistic nuances of various digital coins' online communities and provides a deeper understanding of their interactions in the cryptocurrency space.
Burak Öz, Jonas Gebele, Parshant Singh, Filip Rezabek · 5 authors
Maximal Extractable Value (MEV) searching has gained prominence on the Ethereum blockchain since the surge in Decentralized Finance activities. In Ethereum, MEV extraction primarily hinges on fee payments to block proposers. However, in First-Come-First-Served (FCFS) blockchain networks, the focus shifts to latency optimizations, akin to High-Frequency Trading in Traditional Finance. This paper illustrates the dynamics of the MEV extraction game in an FCFS network, specifically Algorand. We introduce an arbitrage detection algorithm tailored to the unique time constraints of FCFS networks and assess its effectiveness. Additionally, our experiments investigate potential optimizations in Algorand's network layer to secure optimal execution positions. Our analysis reveals that while the states of relevant trading pools are updated approximately every six blocks on median, pursuing MEV at the block state level is not viable on Algorand, as arbitrage opportunities are typically executed within the blocks they appear. Our algorithm's performance under varying time constraints underscores the importance of timing in arbitrage discovery. Furthermore, our network-level experiments identify critical transaction prioritization strategies for Algorand's FCFS network. Key among these is reducing latency in connections with relays that are well-connected to high-staked proposers.
A supply chain is a logistic system that converts raw materials into finished products and distributes them to customers. The system usually consists of material producers, factories, organisations, sellers, consumers as well as transportation. Data and information such as manufacturing data and shipment data also form an important part of the supply chain system and is usually maintained centrally by one entity. Centralisation can cause several problems in supply chain system management, such as single point of failure, corruption, and data tampering. Blockchain has for the past years emerged as a decentralised technology that can help solve the problem of centralisation. The research, therefore, explores the feasibility of using blockchain technology in the supply chain system focusing on coffee produce and shipment. We analyse the suitability of blockchain to the supply chain. We then designed and implemented a smart contract using Solidity, which runs on an Ethereum-based blockchain to show that supply chain data can be traced and blockchain can be used for supply chain purposes.
Aarohi Bhand, Pranjal Bharsakale, Swaraj Deshmane, Manisha Mali
With 1.07% of the country's overall GDP coming from fishing, the fishing industry has huge significance in India. The supply chain and overall exports to foreign nations will be improved by revolutionizing this sector of the economy. Consumers are nowadays very much concerned about their health and in knowing about the quality of the goods they purchase or consume, particularly when it comes to nutrition. Fish must be consumed within a certain time frame and under the right circumstances because it is a perishable food. The government officials must also keep an eye on illicit fishing, which is another serious problem that disrupts ecosystems in many different places. In other words, consumers, the government, and society in general demand transparency throughout the entire value chain of fish products. In this research, the use of blockchain using Ethereum-based smart contracts to trace fish packages back and forth throughout the entire fisheries value chain is explored.
Mathew Fukuzawa, Brandon M. McConnell, Michael G. Kay, Kristin Thoney-Barletta · 5 authors
Purpose Demonstrate proof-of-concept for conducting NFL Draft trades on a blockchain network using smart contracts. Design/methodology/approach Using Ethereum smart contracts, the authors model several types of draft trades between teams. An example scenario is used to demonstrate contract interaction and draft results. Findings The authors show the feasibility of conducting draft-day trades using smart contracts. The entire negotiation process, including side deals, can be conducted digitally. Research limitations/implications Further work is required to incorporate the full-scale depth required to integrate the draft trading process into a decentralized user platform and experience. Practical implications Cutting time for the trade negotiation process buys decision time for team decision-makers. Gains are also made with accuracy and cost. Social implications Full-scale adoption may find resistance due to the level of fan involvement; the draft has evolved into an interactive experience for both fans and teams. Originality/value This research demonstrates the new application of smart contracts in the inter-section of sports management and blockchain technology.
feta cheese is a Greek protected designation of origin (PDO) product that is produced in three main phases: milk collection, cheese preparation and maturation, and product packaging. Each phase must be aligned with quantitative rules, stemming from the legislation framework and best practices. The production complexity, the increased production cost, centralised and monolithic traceability systems, and the lack of a systematic monitoring framework have made dairy products a commodity with increased frequency of food fraud. Given the context of the dairy section in Greece, this study aims to examine (a) whether it is possible to model the end-to-end process of PDO feta cheese considering production rules to develop a trustworthy blockchain-based traceability system (b) how to associate the (‘easy-to-retrieve’, operational) traceability data with the (difficult-to-assess) product characteristics meaningful to the consumer, (c) how to design a technical solution ensuring that information is accessible by the stakeholders and the consumer, while minimising blockchain-related delay, and (d) how to design a graphical user interface and offer tools to consumers so that traceability information is communicated effectively and they can verify it through access to the blockchain. In terms of methods, we analyse and model the process steps, identify measurable, operational parameters and translate the legislative framework into rules. These rules are designed and codified as blockchain smart contracts that ensure the food authenticity and compliance with legislation. The blockchain infrastructure consists of the private Quorum blockchain that is anchored to the public infrastructure of Ethereum. Mechanisms to address scalability in terms of dynamic data volumes, effective data coding, and data verification at the edge as well as relevant limitations are discussed. Consumers are informed about traceability information by using QR codes on food packaging and can verify the data using the blockchain tools and services.
The wheat crop that fulfills 35% of human food demand is facing several problems due to a lack of transparency, security, reliability, and traceability in the existing agriculture supply chain. Many systems have been developed for the agriculture supply chain to overcome such issues, however, monopolistic centralized control is the biggest hurdle to realizing the use of such systems. It has eventually gained consumers' trust in branded products and rejected other products due to the lack of traceable supply chain information. This study proposes a blockchain-based framework for supply chain traceability which provides trustable, transparent, secure, and reliable services for the wheat crop. A crypto token called wheat coin (WC) has been introduced to keep track of transactions among the stakeholders of the wheat supply chain. Moreover, an initial coin offering (ICO) of WC, crypto wallets, and an economic model are proposed. Furthermore, a smart contract-based transaction system has been devised for the transparency of wheat crop transactions and conversion of WC to fiat and vice versa. We have developed the interplanetary file system (IPFS) to improve data availability, security, and transparency which stores encrypted private data of farmers, businesses, and merchants. Lastly, the results of the experiments show that the proposed framework shows better performance as compared to previous crop supply chain solutions in terms of latency to add-blocks, per-minute transactions, average gas charge for the transaction, and transaction verification time. Performance analysis with Bitcoin and Ethereum shows the superior performance of the proposed system.
As of July 15, 2023, Ethererum, which is a Proof-of-Stake (PoS) blockchain [1] has around 410 Billion USD in total assets on chain (popularly referred to as total-value-locked, TVL) but has only 33 Billion USD worth of ETH staked in securing the underlying consensus of the chain [2]. A preliminary analysis might suggest that as the amount staked is far less (11x less) than the value secured, the Ethereum blockchain is insecure and "over-leveraged" in a purely cryptoeconomic sense. In this work, we investigate how Ethereum, or, more generally, any PoS blockchain can be made secure despite this apparent imbalance. Towards that end, we attempt to formalize a model for analyzing the cryptoeconomic safety of PoS blockchain, which separately analyzes the cost-of-corruption, the cost incurred by an attacker, and the profit-from-corruption, the profit gained by an attacker. We derive sharper bounds on profit-from-corruption, as well as new confirmation rules that significantly decrease this upper-bound. We evaluate cost-of-corruption and profit-from-corruption only from the perspective of attacking safety. Finally, we present a new "insurance" mechanism, STAKESURE, for allocating the slashed funds in a PoS system, that has several highly desirable properties: solving common information problem in existing blockchains, creating a mechanism for provably safe bridging, and providing the first sharp solution for automatically adjusting how much economic security is sufficient in a PoS system. Finally, we show that the system satisfies a notion of strong cryptoeconomic safety, which guarantees that no honest transactor ever loses money, and creates a closed system of Karma, which not only ensures that the attacker suffers a loss of funds but also that the harmed parties are sufficiently compensated.
The use of blockchains in the Internet of Things is extremely promising, as it gives connected things the possibility to send and receive payments or tamper-proof data. In the last years, FlyClient has emerged in the literature as a technique for allowing resource constrained devices to verify blockchain transactions. FlyClient is based on Merkle Mountain Ranges (MMRs) and probabilistic sampling, and it allows us to develop blockchain clients whose resource consumption is sublinear with the length of the chain. However, this comes at the cost of a change in the blockchain format, which leads to forks that are politically expensive, because they require 51% consensus. In this paper we explore the possibility of fork-free FlyClient verification methods that leverage smart contract programming. Smart contracts are able to add functionalities to a blockchain without needing forks. This raises several and novel technical issues that we address in the paper. We show that fork-free sublinear clients are feasible without trusting the nodes that invoke the smart contract methods, as long as the smart contract language provides a means to access the most recent block or its hash. As a proof of concept we propose SmartFly, a fork-free FlyClient verification system for the Ethereum Classic blockchain. We measure several performance metrics of SmartFly, proving that it is succinct in storage and bandwidth consumption and economically bearable (about 38 euros per day to maintain the whole system).
The cryptocurrency market has experienced remarkable growth and garnered widespread attention in recent years. Ethereum, as one of the most well-known cryptocurrencies, has faced criticism for its network congestion and high transaction fees. To address this issue, developers have proposed various scalability solutions. In this paper, we compare two different network solutions, Polygon and Arbitrum, to the Ethereum. Based on the research methodology of complex networks, this study encompasses various stages ranging from data collection and processing to the modeling and analysis of transaction networks. Our findings reveal that Arbitrum shows an advantage over Polygon when comparing the networks at the same transaction volume. With sufficient development, Rollups has the potential to become the preferred scaling solution for Ethereum. Additionally, our analysis illustrates that all transaction networks exhibit a small-world phenomenon when they reach a significant size.
Andrea De Salve, Alessandro Brighente, Mauro Conti
Modeling and predicting the behavior of nodes and users in blockchains provide opportunities for business strategy optimization. Indeed, the number of interactions of a node is strictly related to its balance and its prediction may be used for analytics purposes and investment strategies. However, the amount and diversity of information stored on the blockchain demand advanced tools for the modeling and analysis of blockchain data. Such tools should be able to capture the dynamicity and interaction of multiple independent actors, considering a large number of variables and dynamic interaction graph topologies. This is exacerbated by the use of smart contracts, programs stored in blockchain blocks that bring automation to blockchain’s operations and thus increasing the variability of the resulting interaction graphs. Existing modeling methodologies are unable to keep track of all these details, as they are not able to capture the temporal variability of the network. In this paper, we propose a novel framework for modeling and predicting the behavior of smart contracts on a blockchain. We propose the concept of temporal smart contracts networks, i.e., graphs representing the temporal evolution of interactions and data flow. Our framework allows the creation of temporal smart contract networks with different granularity levels by considering different interaction patterns between smart contracts, externally owned accounts, and internal transactions. Thanks to these graphs, we are able to model features such as the node in degree and amount of ether received by a smart contract, which are directly related to its behavior. We incorporate our modeling approach in Ethereum Data Inspection Tool (EDIT), a novel tool able to model interactions and predict them based on historical data. We test different machine learning models to predict features extracted by EDIT, hence allowing for the prediction of the overall behavior of the smart contract. We test EDIT on the Ethereum blockchain and model several temporal smart contracts networks, which represent the interactions and the data flow resulting from about 4 000 000 consecutive blocks. The evaluation of different real case studies shows that the proposed framework is able to predict, with a mean absolute error close to 1%, the evolution of several interesting properties (e.g., amount of received ether) related to both accounts and smart contracts.