Bitcoin network analysis: Spring 2020
This is the page of the Bitcoin Network analysis course, part of the Master 2 Finance Technology Data at Paris 1 Pantheon Sorbonne.
The class covers: 1)Fundamentals of Network Science, in particular network discription, node centralities, community organisation, etc. 2)Network Analysis of the Bitcoin Blockchain Data.
Objectives
This class is thought to provide an introduction to network science, and its application to financial transactions in general, and cryptocurrencies in particular.
Overview of the course
The course is composed of 9h of classes, TD/TP.
Slides
Evaluation: Project
Students work in groups of 2 or 3.
They have to send me their project before Sunday, March 8, 2020, 23:59.
The details about the project can be found there: Project description.
They have to send me their project before Sunday, March 8, 2020, 23:59.
The details about the project can be found there: Project description.
Resources
Gephi
To download Gephi network visualization software: LINK
An introduction on how to use Gephi.
Networkx
Introduction to networkx python library: LINK
A notebook example on how to describe a network global properties using networkx: LINK
A notebook example on how to do simple link prediction, including using community detection with networkx and sklearn: LINK
Network of known actors
Transactions between known actors from August 2 2016 to August 4 2016
File
Network of most active clusters
Transactions between clusters with large exchange relations (>100 btc) from August 2 2016 to August 4 2016
File
Last 1 Million transactions in the database
Data in csv
Example of filtering: Notebook
List of all known actors sorted by number of addresses in the whole dataset
File
To download Gephi network visualization software: LINK
An introduction on how to use Gephi.
Networkx
Introduction to networkx python library: LINK
A notebook example on how to describe a network global properties using networkx: LINK
A notebook example on how to do simple link prediction, including using community detection with networkx and sklearn: LINK
Network of known actors
Transactions between known actors from August 2 2016 to August 4 2016
File
Network of most active clusters
Transactions between clusters with large exchange relations (>100 btc) from August 2 2016 to August 4 2016
File
Last 1 Million transactions in the database
Data in csv
Example of filtering: Notebook
List of all known actors sorted by number of addresses in the whole dataset
File