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Digital Currency Network Centrality Measure (DCNC): A New Centrality Measure for Cryptocurrency Datasets
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 67 |
| Year of Publication: 2025 |
| Authors: Syed Ur Rehman, Ubaida Fatima |
10.5120/ijca2025924486
|
Syed Ur Rehman, Ubaida Fatima . Digital Currency Network Centrality Measure (DCNC): A New Centrality Measure for Cryptocurrency Datasets. International Journal of Computer Applications. 186, 67 ( Feb 2025), 41-46. DOI=10.5120/ijca2025924486
Abstract
Digital currency transaction graphs can be analyzed through Social Network analysis (SNA) techniques to understand complex networks. The importance of nodes was determined through many popular centrality measures including Degree centrality (DC), eigenvector centrality (EVC), betweenness centrality (BC) and closeness centrality (CC).A Novel centrality metrics: Digital currency network centrality measures(DCNC), which is especially conceptualized and designed to measure the importance of nodes based on their involvement in digital currency transactions networks. By Pearson's correlation coefficient (r), DCNC is correlates with standard centrality measures, a strong positive correlation is observed confirming that DCNC accurately correlates with standard metrics and giving more information about digital currencies networks. Results are validate by three real life datasets and two small graphs.
References
- Maesa,D.,et al (2018) Data driven analysis of Bitcoin properties: exploiting theusers graph.
- Parente, M., Rizzuti, L., & Trerotola, M. (2024). A profitable trading algorithm for cryptocurrencies using a Neural Network model. Expert Systems with Applications,238(PA),121806.https://doi.org/10.1016/j.eswa.2023.121806
- Nair, M., Marie, M. I., & Abd-Elmegid, L. A. (2023). Prediction of Cryptocurrency Price using Time Series Data and Deep Learning Algorithms. International Journal of Advanced Computer Science and Applications, 14(8), 338–347. https://doi.org/10.14569/IJACSA.2023.0140837
- Raza, S. A. (2021). Google Trends and Cryptocurrencies: A Nonparametric Causality-In -Quantiles Analysis Abstract: Paper Knowledge. Toward a Media History of Documents, 3(2), 6.
- Pilkington M. (2016). Blockchain technology: principles and applications.
- Hancock M, Vaizey E. (2016). Distributed ledger technology: beyond block chain. Government Office for Science UK. Available at https://www.gov.uk/government/news/distributed-ledger-technology-beyond-block-chain.
- Pocher,N et al (2023) Detecting anomalous cryptocurrency transactions: An AML/CFT Application of machine learning based forensics .
- Baiod,W , et al. (2021). Blockchain technology and its applications across multiple domains: A survey.
- Goldenberg,D (2024). Social Network Analysis: From Graph Theory to Applications with Python.
- Albert, R. (2005). Scale-free networks in cell biology. Journal of Cell Science, 118(21), 4947-4957.
- Scott, J. (1988). Social network analysis. Sociology, 22(1), 109-127.
- Engel,J.,et al (2021) Network Analysis for Economics and Finance: An Application to Firm Ownership.
- S. Boccaletti,.et al (2006) Complex network :Structure and dynamics
- Drozdz,S,.et al (2021) Complexity in Economic and Social Systems
- Motie,S & Raahemi,B (2024) Financial fraud detection using graph neural networks: A systematic review
- Fouad,H & Rego,N (2024) Can social network analysis contribute to supply chain management? A systematic literature review and bibliometric analysis
- Chen, D., et al. (2020). A survey of blockchain applications in different domains. Blockchain: Research and Applications.
- Huang,Y,.et al.(2024)Identifying key players in complex networks via network entanglement
- Lischke, M., & Fabian, B. (2016). Analyzing the Bitcoin network: The first four years. Future Internet, 8(1), 7.
- Ron, D., Shamir, A. (2013). Quantitative analysis of the full bitcoin transaction graph. Financial Cryptography and Data Security.Li, Y., et al. (2024). Advancements in social network analysis for cryptocurrency ecosystems. Journal of Computational Finance
- Sarmah,S,S(2018) Understanding Blockchain Technology.
- Treiblmaier, H., Sillaber, C.The impact of blockchain on e-commerce: A framework for salient research topics https://doi.org/10.1016/j.elerap.2021.101054
- David Vidal-Tomás (2021)Transitions in the cryptocurrency market during the COVID-19 pandemic: A network analysis
- Wu, Jiajing.,et al.(2021). Analysis of cryptocurrency transactions from a network perspective: An overview.
- Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. Bitcoin.org.
- Robins,G & Lusher,D (2010) The Application of Social Network Analysis to Team Sport.
- Fatima,U et al.,(2023) A novel global clustering coefficient-dependent degree centrality (GCCDC) metric for large network analysis using real-world datasets
- KIN-HON HO et al(2024) Exploring Key Properties and Predicting Price Movements of Cryptocurrency Market Using Social Network Analysis
- sign-epinions Social Networks Network Data Repository (networkrepository.com).
- Lusseau et al. (2023) The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations.
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