How Graph Algorithms Answer your Business Questions in Banking and Beyond

Graph algorithms are powerful tools, and there’s a lot of excitement about their applications for data science. It can sometimes be difficult, however - especially for those of us who aren’t data scientists - to know how they might be applied to a particular data set or a specific business problem. There are graph algorithms for centrality and importance measurement, community detection, similarity comparison, pathfinding, and link prediction. Which ones should you use on your data, and which ones might be most useful in answering your business questions?
In this presentation, we’ll look at a few examples of Neo4j graph algorithms, and see how they can be applied to data and business problems from the banking industry. We’ll discuss what kinds of data are appropriate for different types of algorithms, show how to model and structure data to work with graph algorithms, and run through some real-world scenarios demonstrating the use of graph algorithms on a sample banking data set
Webinar with Joe Depeau, Neo4j, April 15, 2020

[@sailibakalkar1536]

I wish you could guide which dataset you have used

[@AndyShelton1]

Can you re-record the missing 5 minutes and insert in video please? That seemed to be the most crucial part of the demo for us beginners

[@sailibakalkar1536]

Can u share the dataset which was used in the demo

[@mayanktripathi4u]

Seems the dataset will be available in the sandbox in some days as it was mentioned in video 54:10

[@sailibakalkar1536]

@@mayanktripathi4u thanks

[@juniebonifacio8206]

Good evening@@mayanktripathi4u may I gently ask if you saw this now released in the sandbox? I signed up today and still do not see it there unfortunately.