Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2024
Abstract
Since its advent in 2009, Bitcoin (BTC) has garnered increasing attention from both academia and industry. However, due to the massive transaction volume, no systematic study has quantitatively measured the asset decentralization degree specifically from a network perspective.In this paper, by conducting a thorough analysis of the BTC transaction network, we first address the significant gap in the availability of full-history BTC graph and network property dataset, which spans over 15 years from the genesis block (1st March, 2009) to the 845651-th block (29, May 2024). We then present the first systematic investigation to profile BTC's asset decentralization and design several decentralization degrees for quantification. Through extensive experiments, we emphasize the significant role of network properties and our network-based decentralization degree in enhancing Bitcoin analysis. Our findings demonstrate the importance of our comprehensive dataset and analysis in advancing research on Bitcoin's transaction dynamics and decentralization, providing valuable insights into the network's structure and its implications. The whole transaction data is available at dataset link.
Keywords
Bitcoin, Dataset, Network Properties, Transaction Network
Discipline
Databases and Information Systems | Finance and Financial Management | OS and Networks
Research Areas
Data Science and Engineering
Publication
2024 IEEE International Conference on Big Data, BigData: Washington, DC, December 15-18: Proceedings
First Page
14
Last Page
23
ISBN
9798350362480
Identifier
10.1109/BigData62323.2024.10825407
Publisher
IEEE
City or Country
Pistacataway
Citation
CHENG, Ling; SHAO, Qian; ZENG, Fengzhu; and ZHU, Feida.
A full-history network dataset for BTC asset decentralization profiling. (2024). 2024 IEEE International Conference on Big Data, BigData: Washington, DC, December 15-18: Proceedings. 14-23.
Available at: https://ink.library.smu.edu.sg/sis_research/10123
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/BigData62323.2024.10825407
Included in
Databases and Information Systems Commons, Finance and Financial Management Commons, OS and Networks Commons