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Analysis of BTC Blockchain Ledger
- Citation Author(s):
- Submitted by:
- Jinyi Guo
- Last updated:
- Fri, 02/01/2019 - 21:16
- DOI:
- 10.21227/pvq9-pz39
- License:
- Categories:
- Keywords:
Abstract
The data contains BTC Blockchain ledger of 100M, from February 3, 2009 to April 25, 2011. It also includes analysis results of the ledger: 10 characteristic indicators and 2 money laundering models. 10 characteristic indicators are ‘top 100 nodes with most in-degree’, ‘top 100 nodes with most out-degree’, ‘nodes have frequent transactions’, ‘a user owns several accounts’, ‘nodes have transactions with large amount of money’, ‘double-umbrella model’, ‘nodes with indirect transaction’, ’ loop network’, ‘nodes with close relationship’, and ‘transactions whose receiver and payer are the same account’. Two money laundering models include ‘Double-umbrella money laundering model’ and ‘Internal transfer money laundering model’.
Blk00000.dat is a part of BTC Blockchain ledger. All files of csv can be used to generate relationship maps via Gephi. 000.csv contains whole transactions of this part of ledger. Indegree100.txt and Outdegree100.txt are results of ‘top 100 nodes with most in-degree’ and ‘top 100 nodes with most out-degree’. Self.sel is ‘transactions whose receiver and payer are the same account’. ‘Loop network’ folder is ‘loop network’. ‘one-many’ folder is ‘a user owns several accounts’. ‘double-umberlla’ folder is ‘double-umbrella model’. Large-amount.docx is ‘nodes have transactions with large amount of money’. Close-relationship.txt is ‘nodes with close relationship’. ‘indirect transaction’ folder is ‘nodes with indirect transaction’. Frequent.docx is ‘nodes have frequent transactions’. ‘Internal transfer money laundering model’ folder is ‘Internal transfer money laundering model’. ‘Double-umbrella money laundering model’ folder is ‘Double-umbrella money laundering model’.
Dataset Files
- analysis of BTC Blockchain ledger.zip (113.14 MB)
- source.zip (1.08 MB)
Comments
I am Dr. Vital, I am working on this research topic. Please provide dataset.