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 webinar, 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.

Joe Depeau
Sr. Presales Consultant, Neo4j

Originally from the USA but now living in the UK, Joe has over 20 years of varied experience in the IT industry across a number of domains and specialties. Most recently, Joe has focused on technical pre-sales and solution architecture in the data and analytics space. When not geeking out over data and technology he enjoys camping, tending to his garden and allotment, reading, and playing boardgames and RPGs. He also bakes a mean cheesecake.


Benoît Simard
Field Engineer, Neo4j

Benoît est un développeur en informatique passionné par les nouvelles technologies et la philosophie du libre.

Ayant réalisé ses études à l'institut des mathématiques appliquées à Angers, son domaine d'expertise s'est tourné vers le domaine du web et des graphes. Evangéliste Neo4j depuis 3 ans, Benoît travaille actuellement chez Neo4j France comme expert et consultant technique. Il aime par ailleurs partager ses connaissances sur les graphes avec la communauté et lors de sessions de formation dédiées.
Kelsey Bieri
Data Governance Analyst, ICC

Kelsey Bieri is a Data Governance Analyst at ICC in the Master Data Management and Data Governance Practice. She has contributed to numerous data governance and data lineage projects in the Banking industry, helping organizations build a better understanding of their data universe. Kelsey holds a degree in Management Information Systems from the College of Business at Ohio University.

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