Exploring EOSIO via Graph Characterization

Zhao, Yijing; Liu, Jieli; Han, Qing; Zheng, Weilin; Wu, Jiajing

doi:10.1007/978-981-15-9213-3_37

Cited by 17 publications

(4 citation statements)

References 13 publications

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“…Zhao et al [88] calculated that the classification coefficient of the coin exchange chart and contract authorization chart is negative, indicating that in coin exchange activities and contract authorization activities, height nodes tend to be connected to small nodes. Motamed and Bahrak [48] calculated the degree matching of the transaction graph to observe whether the account traded with the counterparty.…”

Section: Assortativitymentioning

confidence: 99%

Blockchain Data Analysis from the Perspective of Complex Networks: Overview

Song

Zhang

Wang

et al. 2023

Tsinghua Sci. Technol.

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Cryptocurrency based on blockchain technology has gradually become a choice for people to invest in, and several users have participated in the accumulation of massive transaction data. Complete transaction records in blockchains and the openness of data provide researchers with opportunities to mine and analyze data in blockchains. Network modeling and analysis of cryptocurrency transaction records are common methods in blockchain data analysis. The analysis of attribute graphs can provide insights into various economic indicators, illegal activities, and general Internet security, among others. Accordingly, this article aims to summarize and analyze the literature on cryptocurrency transaction data from the perspective of complex networks. To provide systematic guidance for researchers, we put forward a blockchain data analysis framework based on the introduction of the relevant background and reviewed the work from five aspects: blockchain data model, data acquisition on blockchains, existing analysis tools, available insights, and common analysis methods. For each aspect, we introduce the research problems, summarize the methods, and discuss the results and findings. Finally, we present future research points and several open questions in the study of cryptocurrency transaction networks.

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Section: Assortativitymentioning

confidence: 99%

Blockchain Data Analysis from the Perspective of Complex Networks: Overview

Song

Zhang

Wang

et al. 2023

Tsinghua Sci. Technol.

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“…XBlock-EOS [53] provides an efficient method of data extraction and exploration on the EOSIO blockchain data. Meanwhile, some recent studies characterize different types of activities in EOSIO (such as money transfer and contract invocation) and attempt to identify some bots and fraudulent activities [20,54]. Moreover, other studies focus on detecting vulnerable EOSIO contracts [18,55,19].…”

Section: Eosio Analysismentioning

confidence: 99%

Unravelling Token Ecosystem of EOSIO Blockchain

Zheng¹,

Liu²,

Dai³

et al. 2022

Preprint

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Being the largest Initial Coin Offering project, EOSIO has attracted great interest in cryptocurrency markets. Despite its popularity and prosperity (e.g., 26,311,585,008 token transactions occurred from June 8, 2018 to Aug. 5, 2020), there is almost no work investigating the EOSIO token ecosystem. To fill this gap, we are the first to conduct a systematic investigation on the EOSIO token ecosystem by conducting a comprehensive graph analysis on the entire on-chain EOSIO data (nearly 135 million blocks). We construct token creator graphs, token-contract creator graphs, token holder graphs, and token transfer graphs to characterize token creators, holders, and transfer activities. Through graph analysis, we have obtained many insightful findings and observed some abnormal trading patterns. Moreover, we propose a fake-token detection algorithm to identify tokens generated by fake users or fake transactions and analyze their corresponding manipulation behaviors. Evaluation results also demonstrate the effectiveness of our algorithm.

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“…Zheng et al [36] and others outlined the process and application of obtaining and parsing data from EOS.IO. Zhao et al [37] studied the EOS.IO ecosystem from the perspective of graphical analysis and revealed some abnormal phenomena, such as voting gangs and fake transactions.…”

Section: Related Workmentioning

confidence: 99%

“…Currently, there is little research on graph analysis of the EOS.IO blockchain. Existing research analyses static data over time but does not reflect the dynamic nature of the data [37]. In this article, we use graph G{G1, G2, ⋯ GT} to represent the change in the graph from time 1 to time T, which represents the cumulative modelling of EOS transaction networks of different scales and complexity in a continuous-time range.…”

Section: Network Representation and Modellingmentioning

confidence: 99%

EOS.IO blockchain data analysis

et al. 2021

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Cryptocurrency is a relatively mature application of blockchain technology. The openness of transaction records provides researchers with the opportunity to analyse and compare various cryptocurrencies. The EOS public chain based on EOS. IO supports millions of transactions per second, with billions of transactions, and provides data analysts with a large quantity of analysable transaction data. Combined with the Ethereum platform data of the same period, this paper focuses on the transaction data in the EOS.IO blockchain and analyses the data in the Ethereum and EOS.IO chains from a complex network perspective. By constructing cumulative networks and time-slicing methods, constructing transaction networks of different scales, and dynamically analysing the laws of transaction network changes over time, we find that many transactions, such as transaction volume and transaction relationships, exhibit heavy-tail characteristics and conform to a power-law distribution. In particular, with the change in time and the growth in network scale, the power-law distribution is time-invariant. Our research can verify and predict the progress of blockchain development. Through graph analysis, we also obtained some other observations and discovered some interesting mathematical characteristics that explain the actual interactions that occurred on the blockchain.

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Exploring EOSIO via Graph Characterization

Cited by 17 publications

References 13 publications

Blockchain Data Analysis from the Perspective of Complex Networks: Overview

Blockchain Data Analysis from the Perspective of Complex Networks: Overview

Unravelling Token Ecosystem of EOSIO Blockchain

EOS.IO blockchain data analysis

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