Consensus Blockchain Decentralization

This repository provides a CLI tool for analyzing the block production of various blockchains and measuring their subsequent levels of decentralization. Please refer to the project's documentation pages for details on its architecture, required input, produced output, and more.

Currently, the supported blockchains are:

• Bitcoin
• Bitcoin Cash
• Cardano
• Dogecoin
• Ethereum
• Litecoin
• Tezos
• Zcash

We intend to add more ledgers to this list in the future.

Installation

To install the tool, simply clone this project:

git clone https://github.com/Blockchain-Technology-Lab/consensus-decentralization.git

The requirements file lists the dependencies of the project. Make sure you have all of them installed (preferably within a virtualenv) before running the scripts. To install all of them in one go, run the following command from the root directory of the project:

python -m pip install -r requirements.txt

Run the tool

Place all raw data (which could be collected from BigQuery for example) in the raw_block_data directory, each file named as <project_name>_raw_data.json (e.g. bitcoin_raw_data.json). By default, there is a (very small) sample input file for some supported projects. To use the samples, remove the prefix sample_. For more extended raw data and instructions on how to retrieve it, see here.

Run python run.py --ledgers <ledger_1> ... <ledger_n> --timeframe <timeframe> to produce a csv of the mapped data. Note that both arguments are optional, so it's possible to omit one or both of them (in which case the default values will be used). Specifically:

• The ledgers argument accepts any number of supported ledgers (case-insensitive). For example, --ledgers bitcoin runs the analysis for Bitcoin, --ledgers Bitcoin Ethereum Cardano runs the analysis for Bitcoin, Ethereum and Cardano, etc. Ledgers with more words should be defined with an underscore; for example Bitcoin Cash should be set as bitcoin_cash.
• The timeframe argument should be of the form YYYY-MM-DD (month and day can be omitted). For example, --timeframe 2022 runs the analysis for the year 2022, --timeframe 2022-02 runs it for February 2022, etc.

run.py prints the output of each implemented metric for the specified ledgers and timeframe.

To mass produce and analyze data, omit one or both arguments. If only the ledgers is given, all data since January 2018 for the given ledgers will be analyzed. If only the timeframe is specified, all ledgers will be analyzed for the given timeframe. If no arguments are given, all ledgers will be analyzed for all months since January 2018.

Three files nc.csv, gini.csv, entropy.csv are also created in the output directory, containing the data from the last execution of run.py.

Contributing

Everyone is welcome to contribute ideas, report bugs, and make the code more efficient. We especially welcome contributions to the following areas:

• Add support for a ledger that is not already supported.
• Update and/or add mapping information for a ledger.
• Add a new metric.

For detailed information on how to contribute see the relevant documentation page.

Maintainers

The tool is actively maintained by the following people:

• Dimitris Karakostas
• Christina Ovezik
• Aggelos Kiayias

License

The code of this repository is released under the MIT License. The documentation pages are released under CC BY-SA 4.0.