Drumpfwelt is now hoping that removing the current Russian Ambassador will take away a key agent in the #TrumpRussia investigation, but there’s probably far too much IC information and Kislyak got caught running far too many Trumpian cut-outs. Until then Kislyak needs to stay out of the back seat of his car, lest he have indigestion or a heart attack there.
Less than a month before his father-in-law and future boss was elected president, Jared Kushner finalized a $285 million loan agreement with Deutsche Bank, the global banking behemoth that is was at that time roiled by charges from New York financial regulators that the bank had been involved in Russian money laundering.
According to the Washington Post, Kushner’s relationship with Deutsche Bank has come under scrutiny in the ongoing investigation by special counsel Robert Mueller into Russian interference in the U.S. presidential election. Kushner did not disclose the loan on his personal financial disclosures.
A social network analysis of TrumpWorld can yield interesting infographics that now flood the Web, but whether the data yields actionable intelligence rather than pretty pictures remains to be seen as the critically real(ist) relationships become clearer by the ongoing investigations.
The simplest example of how such analysis can be useful is by looking at weak/strong ties between nodes. For example. Ambassador Kislyak’s departure might make a difference in the #TrumpRussia matter, but probably not because of his absence since he would be a “hostile witness” if not a co-conspirator. Is Putin covering his own ass or Trump’s, by recalling Kislyak.
One of the powers of working with graph databases is the ability to combine disparate datasets and query across them. Today we’ll look at how we can combine the BuzzFeed Trumpworld graph with data about federal government contracts from USASpending.gov, allowing us to examine any government contracts that were awarded to organizations that appear in Trumpworld.
www.lyonwj.com/…
All of the data and queries used here are public, so feel free to reproduce the data in your own instance of Neo4j. Neo4j is open source and free to use, you can get it here. To make working with this data even easier, we’ve created a Neo4j Sandbox Trumpworld example. In addition there are some embedded Neo4j Browser Guides for working with the dataset (introducing Cypher, interesting queries, applying social network analysis techniques to the dataset, etc. )
four investigative journalists from Buzzfeed released an intriguing new dataset called TrumpWorld.
It’s a valiant attempt to document the sprawl of organizations and individuals connected to the new administration. Released as a series of spreadsheets covering more than 1500 entities, the dataset came with a request: that others explore, enrich and extend the data to help build a complete picture of the network surrounding President Trump...
It’s always fun to play with a new dataset, especially one as relevant to current events as the business interests of the US President. But this dataset was interesting because it posed a common graph visualization challenge: the hairball. This is when connections become so dense, they cannot be usefully visualized.