Utilizando Métricas de Centralidade para Analisar a Distribuição de Riqueza em Transações da Blockchain do Bitcoin
Abstract
This study analyzes the application of four centrality metrics in the complex network formed by high-value transactions in the Bitcoin blockchain over a period of 245 days between Jun/2022 and Feb/2023, with the goal of identifying the most important nodes for wealth distribution. The results indicate that addresses that maintain their centrality and importance over time are mainly attributed to large exchanges and custodial entities, which are described in detail in the article. Additionally, the study reveals that between 91% and 94% of the addresses considered central during the analyzed period do not repeat in the results. The study also shows parity among the top results for three centrality metrics used.
References
Nakamoto, S. and Bitcoin, A. (2008). A peer-to-peer electronic cash system. Bitcoin.– URL: https://bitcoin.org/bitcoin.pdf, 4.
Silva, C., Ramos, B., Oliveira, S., & Piccoli, R. (2018). Caracterização da Rede Bitcoin: Uma Visão sobre a Evolução de Blocos, Transações, Endereços e Saldos de 2009 até 2017. In Anais do I Workshop em Blockchain: Teoria, Tecnologias e Aplicações. Porto Alegre: SBC.
Emery, J. A., & Latapy, M. (2021). Full Bitcoin blockchain data made easy. In Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 240-243).
Silva, L. P. Q., de Araújo, A. P., Cota, D. O., Cota, G. O., & Antonio, A. D. A. (2021). Utilizando HMM para previsao de preço e estratégia de investimento em criptomoedas BitCoin. In Anais do IV Workshop em Blockchain: Teoria, Tecnologias e Aplicações (pp. 134-147). SBC.
Ho, K. H., Chiu, W. H., & Li, C. (2020). A Short-Term Cryptocurrency Price Movement Prediction Using Centrality Measures. In 2020 International Conference on Data Mining Workshops (ICDMW) (pp. 369-376). IEEE.
Pereira, D. M., & Couto, R. S. (2022). Using Degree Centrality to Identify Market Manipulation on Bitcoin. In Data Privacy Management, Cryptocurrencies and Blockchain Technology: ESORICS 2021 International Workshops, DPM 2021 and CBT 2021, Darmstadt, Germany, October 8, 2021, Revised Selected Papers (pp. 208-223). Cham: Springer International Publishing.
Tao, B., Dai, H. N., Wu, J., Ho, I. W. H., Zheng, Z., & Cheang, C. F. (2021). Complex network analysis of the bitcoin transaction network. IEEE Transactions on Circuits and Systems II: Express Briefs, 69(3), 1009-1013.
Kondor, D., Pósfai, M., Csabai, I., & Vattay, G. (2014). Do the rich get richer? An empirical analysis of the Bitcoin transaction network. PloS one, 9(2), e86197.
Albert, R., & Barabási, A. L. (2002). Statistical mechanics of complex networks. Reviews of modern physics, 74(1), 47.
Newman, M. E. (2003). The structure and function of complex networks. SIAM review, 45(2), 167-256.
Ebrahimi, M. S., & Babveyh, A. (2018). Predicting User Performance and Bitcoin Price Using Block Chain Transaction Network. arXiv preprint arXiv:1804.08044.
Chan, W. K., Chin, J. J., & Goh, V. T. (2020, December). Evolution of Bitcoin addresses from security perspectives. In 2020 15th International Conference for Internet Technology and Secured Transactions (ICITST) (pp. 1-6). IEEE.
Wuille, P. (2012). Hierarchical Deterministic Wallets. Bitcoin Improvement Proposal 32 (BIP0032). Bitcoin Github, November.
Palatinus, M., & Rusnak, P. (2014). Multi-account hierarchy for deterministic wallets. Bitcoin Improvement Proposal 44 (BIP0044). Bitcoin Github, April.
Hagberg, A., Swart, P., & S Chult, D. (2008). Exploring network structure, dynamics, and function using NetworkX (No. LA-UR-08-05495; LA-UR-08-5495). Los Alamos National Lab.(LANL), Los Alamos, NM (United States).
Remy, C., Rym, B., & Matthieu, L. (2018). Tracking bitcoin users activity using community detection on a network of weak signals. In Complex Networks & Their Applications VI: Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications) (pp. 166-177). Springer International Publishing.
Di Francesco Maesa, D., Marino, A., & Ricci, L. (2018). Data-driven analysis of bitcoin properties: exploiting the users graph. International Journal of Data Science and Analytics, 6, 63-80.
Peshov, H., Todorovska, A., Marojevikj, J., Spirovska, E., Rusevski, I., Angelovski, G., ... & Trajanov, D. (2023, January). Using Centrality Measures to Extract Knowledge from Cryptocurrencies’ Interdependencies Networks. In ICT Innovations 2022. Reshaping the Future Towards a New Normal: 14th International Conference, ICT Innovations 2022, Skopje, Macedonia, September 29–October 1, 2022, Proceedings (pp. 76-90). Cham: Springer Nature Switzerland.
Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. In Proceedings of the international AAAI conference on web and social media (Vol. 3, No. 1, pp. 361-362).
Simoes, J. E., Ferreira, E., Menasche, D. S., & Campos, C. A. (2021). Blockchain privacy through merge avoidance and mixing services: a hardness and an impossibility result. ACM SIGMETRICS Performance Evaluation Review, 48(4), 8-11.
Ferrin, D. (2015). A preliminary field guide for bitcoin transaction patterns. In Proc. Texas Bitcoin Conf..
Silva, C., Ramos, B., Oliveira, S., & Piccoli, R. (2018). Caracterização da Rede Bitcoin: Uma Visão sobre a Evolução de Blocos, Transações, Endereços e Saldos de 2009 até 2017. In Anais do I Workshop em Blockchain: Teoria, Tecnologias e Aplicações. Porto Alegre: SBC.
Emery, J. A., & Latapy, M. (2021). Full Bitcoin blockchain data made easy. In Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 240-243).
Silva, L. P. Q., de Araújo, A. P., Cota, D. O., Cota, G. O., & Antonio, A. D. A. (2021). Utilizando HMM para previsao de preço e estratégia de investimento em criptomoedas BitCoin. In Anais do IV Workshop em Blockchain: Teoria, Tecnologias e Aplicações (pp. 134-147). SBC.
Ho, K. H., Chiu, W. H., & Li, C. (2020). A Short-Term Cryptocurrency Price Movement Prediction Using Centrality Measures. In 2020 International Conference on Data Mining Workshops (ICDMW) (pp. 369-376). IEEE.
Pereira, D. M., & Couto, R. S. (2022). Using Degree Centrality to Identify Market Manipulation on Bitcoin. In Data Privacy Management, Cryptocurrencies and Blockchain Technology: ESORICS 2021 International Workshops, DPM 2021 and CBT 2021, Darmstadt, Germany, October 8, 2021, Revised Selected Papers (pp. 208-223). Cham: Springer International Publishing.
Tao, B., Dai, H. N., Wu, J., Ho, I. W. H., Zheng, Z., & Cheang, C. F. (2021). Complex network analysis of the bitcoin transaction network. IEEE Transactions on Circuits and Systems II: Express Briefs, 69(3), 1009-1013.
Kondor, D., Pósfai, M., Csabai, I., & Vattay, G. (2014). Do the rich get richer? An empirical analysis of the Bitcoin transaction network. PloS one, 9(2), e86197.
Albert, R., & Barabási, A. L. (2002). Statistical mechanics of complex networks. Reviews of modern physics, 74(1), 47.
Newman, M. E. (2003). The structure and function of complex networks. SIAM review, 45(2), 167-256.
Ebrahimi, M. S., & Babveyh, A. (2018). Predicting User Performance and Bitcoin Price Using Block Chain Transaction Network. arXiv preprint arXiv:1804.08044.
Chan, W. K., Chin, J. J., & Goh, V. T. (2020, December). Evolution of Bitcoin addresses from security perspectives. In 2020 15th International Conference for Internet Technology and Secured Transactions (ICITST) (pp. 1-6). IEEE.
Wuille, P. (2012). Hierarchical Deterministic Wallets. Bitcoin Improvement Proposal 32 (BIP0032). Bitcoin Github, November.
Palatinus, M., & Rusnak, P. (2014). Multi-account hierarchy for deterministic wallets. Bitcoin Improvement Proposal 44 (BIP0044). Bitcoin Github, April.
Hagberg, A., Swart, P., & S Chult, D. (2008). Exploring network structure, dynamics, and function using NetworkX (No. LA-UR-08-05495; LA-UR-08-5495). Los Alamos National Lab.(LANL), Los Alamos, NM (United States).
Remy, C., Rym, B., & Matthieu, L. (2018). Tracking bitcoin users activity using community detection on a network of weak signals. In Complex Networks & Their Applications VI: Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications) (pp. 166-177). Springer International Publishing.
Di Francesco Maesa, D., Marino, A., & Ricci, L. (2018). Data-driven analysis of bitcoin properties: exploiting the users graph. International Journal of Data Science and Analytics, 6, 63-80.
Peshov, H., Todorovska, A., Marojevikj, J., Spirovska, E., Rusevski, I., Angelovski, G., ... & Trajanov, D. (2023, January). Using Centrality Measures to Extract Knowledge from Cryptocurrencies’ Interdependencies Networks. In ICT Innovations 2022. Reshaping the Future Towards a New Normal: 14th International Conference, ICT Innovations 2022, Skopje, Macedonia, September 29–October 1, 2022, Proceedings (pp. 76-90). Cham: Springer Nature Switzerland.
Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. In Proceedings of the international AAAI conference on web and social media (Vol. 3, No. 1, pp. 361-362).
Simoes, J. E., Ferreira, E., Menasche, D. S., & Campos, C. A. (2021). Blockchain privacy through merge avoidance and mixing services: a hardness and an impossibility result. ACM SIGMETRICS Performance Evaluation Review, 48(4), 8-11.
Ferrin, D. (2015). A preliminary field guide for bitcoin transaction patterns. In Proc. Texas Bitcoin Conf..
Published
2023-05-22
How to Cite
SCHEIBE, Guilherme A.; MARQUES-NETO, Humberto T..
Utilizando Métricas de Centralidade para Analisar a Distribuição de Riqueza em Transações da Blockchain do Bitcoin. In: BLOCKCHAIN WORKSHOP: THEORY, TECHNOLOGY AND APPLICATIONS (WBLOCKCHAIN), 6. , 2023, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 99-111.
DOI: https://doi.org/10.5753/wblockchain.2023.769.
