GraphSense is an open source platform for analyzing cryptocurrencies such as Bitcoin.

  • Address Clustering: partition the set of addresses observed in a cryptocurrency ecosystem into maximal subsets (clusters) that are likely to be controlled by the same real-world entity.

  • Micro- and Macroscopic Analysis: inspect main cryptocurrency entities (block, transaction, address) and compute summary statistics over the entire blockchain.

  • Network Perspective: apply a network-centric perspective and traverse currency flows between addresses and clusters.

  • Horizontal Scalability: cryptocurrency blockchains are growing and new currencies appear on the horizon. To make GraphSense future-proof, it is built on Apache Spark and Cassandra for horizontal scalability.

Technical Architecture

GraphSense is built on scalable and distributed cluster technology and therefore requires a number of software components. They must be setup and/or executed in the following order:

  • bitcoin-client: a Docker container encapsuling the most-recent Bitcoin client version

  • datafeed: a component for ingesting raw blockchain data and exchange rates into Cassandra

  • transformation: a Spark pipeline for computing statistics and network representations from raw blockchain data stored in Cassandra.

  • rest-api: an API for retrieving data from the underlying Cassandra store

  • dashboard: a user-interface allowing search, inspection, and traversal of cryptocurrency entities


The following example shows details about an example Bitcoin address.



Some more technical details about GraphSense are described here; please cite as:

    title={O Bitcoin Where Art Thou? Insight into Large-Scale Transaction Graphs.},
    author={Haslhofer, Bernhard and Karl, Roman and Filtz, Erwin},
    booktitle={SEMANTiCS (Posters, Demos)},

So far, GraphSense has been used for computing statistics in the following scientific papers:

Filtz, E., Polleres, A., Karl, R., Haslhofer, B.: Evolution of the Bitcoin Address Graph - An Exploratory Longitudinal Study. International Data Science Conference (DSC 2017), Salzburg, Austria, 2017. (pdf)