I was initially going to give my analysis along with the release of the data set. I have since decided to give myself (and the public) more time to investigate the data so as to iron out potential problems. So no accusations or blame as of yet, just lots of open questions.

As usual my intention is to inform, not to incite. The connections between individuals and companies are only listed for their relevancy within the indie/video game industry. There may obviously be errors in the data, if you notice any please let me know.

Where is the data from?

Most of the data was already publicly available on DeepFreeze, there is some additional research in this which I will go into detail at another point. I asked bonegolem for a dump of DF and he happily provided me with it. I extracted the relevant data into a spreadsheet and combined it with my prior work data.

How did you manipulate the data?

I’ve manually only selected entries labeled corruption or cronyism (including ones where a disclaimer was later added) and I have then manually gone through every entry and checked the archives as well as the claim and then used this data to build relationships within the spreadsheet.

What is the structure of the spreadsheet?

There are 3 node types: Person, Group(Company, Advocacy Group, Event, Show) , Product

The relationships (represented as sheets in the document) between these node types are grouped as follows:

Financial: fin_person_group, fin_person_person, fin_person_product, fin_group_group, fin_group_person, fin_group_product

Relationship: relationship

Coverage: coverage_person, coverage_group, coverage_product

Involvement: involvement_group, involvement_product

What can I do with this data?

If you’re interested to make your own graphs & analyses you can simply download & install Neo4j and then go ahead and download to each of the following sheets CSV-files (into your local c:

eo4j-directory): fin_person_group, fin_person_person, fin_person_product, fin_group_group, fin_group_person, fin_group_product, relationship, coverage_person, coverage_group, coverage_product, involvement_group, involvement_product, product

After you’ve done all that you will need to rename all of your csv-Files so that it only says fin_person_group.csv (for example) and you will also need to manually remove the entires in the product-csv-File without a Company (I haven’t found a better workaround as of yet)

Once you’ve done that you can run the commands from this pastebin: http://pastebin.com/XzBwcx3e

If you’ve done everything right you can now run some of these commands and watch the magic happen:

MATCH (n) — (p1:Person) — (p2:Person)-[m:MEMBER]->(g2:Group)

WHERE g2.name = “Kotaku”

RETURN p1,p2,g2,m,n

MATCH (n) — (p1:Person) — (p2:Person)-[m:MEMBER]->(g2:Group)

WHERE g2.name = “Polygon”

RETURN p1,p2,g2,m,n

MATCH (p1:Person) — (p2:Person)

WHERE p1.name IN [“Rowan Kaiser”,”Jenn Frank”,”Rami Ismail”,”Leigh Alexander”,”Katherine Cross”]

return p1,p2

Enjoy!

Here’s the data: