Bitcoin Data – Neo4j 

In Part 1 of this series, we showed how we could use Linked Data Analysis to look at Bitcoin transactions. The data contained in the Bitcoin (BTC) network is difficult to analyze manually, but can yield a high number of relevant information. In Part 2 of this series, we looked at how we harvested some Bitcoin data, prepared it for loading into Neo4j and then finally, loaded the data. Using Neo4j, we modeled the BTC data as a graph encapsulating the relationships in the data – for example the relationships between bitcoins, transactions, blocks, and wallets. Using Neo4j’s Cypher language, we can query the data looking for patterns of activity, easily visualize the data and provide the data out to be analyzed with machine learning algorithms.

In this post, we will look at at how we can query the data using the Neo4j browser to identify questionable patterns of activity.

Neo4j in Action by Top IT Books
Neo4j in Action is a comprehensive guide to designing, implementing, and querying graph data using Neo4j. Using hands-on examples, you’ll learn to model graph domains naturally with Neo4j graph structures. The book explores the full power of native Java APIs for graph data manipulation and querying. It also covers Cypher, Neo4j’s graph query language. Along the way, you’ll learn how to integrate Neo4j into your domain-driven app using Spring Data Neo4j, as well as how to use Neo4j in standalone server or embedded modes.
The Crunchbase Graph by Jeanin
Through that series of article on Crunchbase we have shown the typical parts of a graph project. We started with data that was not in a graph format. First we modeled it. Then we loaded it in a Neo4j graph database. Finally we were able to analyse the result. By combining graph analytics and visualization, we discovered market trends, how to make investment suggestions and how to identify influential investors. We hope it will inspire you to look at the graph in your own data soon!