Analyzing the Buzzfeed Trumpworld Graph

With the rise of scary politics world-wide, we want to see how we can use graph technology to get more insights in the myriad connections the political leaders have with companies, banks, news, and other organizations.

If the data is available it is easy to import it into Neo4j and start querying it to get new insights. Then we can use new data sources to add to the graph, enriching it to see the bigger picture.

Today we want to use the TrumpWorld dataset published by BuzzFeed as an example to import, query and extend.

As part of that, we'll also use some graph algorithms to identify core people and companies in the data which might have gone unnoticed otherwise.

If you know anyone interested in data journalism or investigating networks in society, please bring them along.

Here is what we did so far, but there will be much more at the meetup:
https://neo4j.com/blog/buzzfeed-trumpworld-dataset-neo4j/

Speaker: Michael Hunger, Graph Addict at Neo4j

Michael enjoys making Neo4j users happy and successful.

Heading developer relations is a perfect job for him, that allows coding, writing and speaking about graph related topics at the same time.

He's also leading the Spring Data Neo4j project, is the core maintainer of the APOC procedure library for Neo4j, the Spark Connector and many more projects.

Find him @mesirii (http://twitter.com/mesirii) on Twitter or on http://neo4j.com/slack