research-article

A pareto front-based metric to identify major bitcoin networks influencers

Authors:

Jonathan Gillett,

Shahryar Rahnamayan,

Masoud Makrehchi,

Azam Asilian BidgoliAuthors Info & Claims

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Pages 1395 - 1401

https://doi.org/10.1145/3377929.3398096

Published: 08 July 2020 Publication History

Abstract

Bitcoin is a novel digital currency that relies on cryptography instead of a central authority to verify transactions. Without a central authority, Bitcoin requires a complete list of all transactions to be made public so that they can be verified by all users. The major network influencers in a Bitcoin network are defined as users that accumulate a disproportionate amount of wealth compared to others. However, there are some defined metrics to identify major network influencers, considering multiple criteria can improve the detection task. In this paper, a multi-criteria metric is applied to identify the major network influencers based on the history of their activities recorded on the blockchain. The proposed metric is based on the Pareto front on multiple criteria, the maximum increase in wealth with the least amount of activity using non-dominated sorting inspired from multi-objective optimization. The provided descriptive statistics on extracted data demonstrates the efficiency of the proposed metric on identification of major influencers.

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Cited By

Alofi ABokhari MBahsoon RHendley R(2024)Self-Optimizing the Environmental Sustainability of Blockchain-Based SystemsIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.33258819:3(396-408)Online publication date: May-2024
https://doi.org/10.1109/TSUSC.2023.3325881
Alofi ABokhari MBahsoon RHendley R(2022)Optimizing the Energy Consumption of Blockchain-Based Systems Using Evolutionary Algorithms: A New Problem FormulationIEEE Transactions on Sustainable Computing10.1109/TSUSC.2022.31604917:4(910-922)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TSUSC.2022.3160491

Index Terms

A pareto front-based metric to identify major bitcoin networks influencers
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

July 2020

1982 pages

ISBN:9781450371278

DOI:10.1145/3377929

General Chair:
Carlos Artemio Coello Coello
CINVESTAV-IPN

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2020

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GECCO '20

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SIGEVO

GECCO '20: Genetic and Evolutionary Computation Conference

July 8 - 12, 2020

Cancún, Mexico

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Alofi ABokhari MBahsoon RHendley R(2024)Self-Optimizing the Environmental Sustainability of Blockchain-Based SystemsIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.33258819:3(396-408)Online publication date: May-2024
https://doi.org/10.1109/TSUSC.2023.3325881
Alofi ABokhari MBahsoon RHendley R(2022)Optimizing the Energy Consumption of Blockchain-Based Systems Using Evolutionary Algorithms: A New Problem FormulationIEEE Transactions on Sustainable Computing10.1109/TSUSC.2022.31604917:4(910-922)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TSUSC.2022.3160491

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