Publication: Detailed analysis of Ethereum network on transaction behavior, community structure and link prediction
Issued Date
2021-01-01
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ISSN
23765992
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2-s2.0-85122434443
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Mahidol University
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SCOPUS
Bibliographic Citation
PeerJ Computer Science. Vol.7, (2021), 21-26
Suggested Citation
Anwar Said, Muhammad Umar Janjua, Saeed Ul Hassan, Zeeshan Muzammal, Tania Saleem, Tipajin Thaipisutikul, Suppawong Tuarob, Raheel Nawaz Detailed analysis of Ethereum network on transaction behavior, community structure and link prediction. PeerJ Computer Science. Vol.7, (2021), 21-26. doi:10.7717/peerj-cs.815 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/76721
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Title
Detailed analysis of Ethereum network on transaction behavior, community structure and link prediction
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Abstract
Ethereum, the second-largest cryptocurrency after Bitcoin, has attracted wide attention in the last few years and accumulated significant transaction records. However, the underlying Ethereum network structure is still relatively unexplored. Also, very few attempts have been made to perform link predictability on the Ethereum transactions network. This paper presents a Detailed Analysis of the Ethereum Network on Transaction Behavior, Community Structure, and Link Prediction (DANET) framework to investigate various valuable aspects of the Ethereum network. Specifically, we explore the change in wealth distribution and accumulation on Ethereum Featured Transactional Network (EFTN) and further study its community structure. We further hunt for a suitable link predictability model on EFTN by employing state-of-the-art Variational Graph Auto-Encoders. The link prediction experimental results demonstrate the superiority of outstanding prediction accuracy on Ethereum networks. Moreover, the statistic usages of the Ethereum network are visualized and summarized through the experiments allowing us to formulate conjectures on the current use of this technology and future development. Subjects Data Mining and Machine Learning, Data Science, Emerging Technologies