Whether you’re new to using graphs in data science, or an expert looking to wring a few extra percentage points of accuracy out of your data, we’re building our Graph Data Science (GDS) library to make your life easier.
Link prediction is all about filling in the blanks – or predicting what’s going to happen next. In a graph, links are the connections between concepts: knowing a friend, buying an item, defrauding a victim, or even treating a disease.
In this 60-minute webinar, we’ll be doing a deep dive into how to use Neo4j and GDS for link prediction. Our newest GDS release (1.7) adds the ability to define and run pipelines for supervised machine learning (ML). This means you can define the features you want to use, and we’ll handle splitting your data, calculating your features, and picking the best model for your problem.
In this deep dive you'll learn:
- How link prediction works
- How to adjust for sparse or biased data
- How to define and run pipelines for supervised ML
- And, see this demonstrated on a real-world data set
Save your spot today for this free webinar.
Alicia Frame is the lead product manager for data science at Neo4j. She's spent the last year translating input from customers, early adopters, and the community into the first truly enterprise product for doing data science with graphs: Neo4j's Graph Data Science Library. She has a phd in computational biology from UNC Chapel Hill, and her background is in data science applications in healthcare and life sciences. She's worked in academia, government, and the private sector to leverage graph techniques for drug discovery, molecular optimization, and risk assessments -- and is super excited to be making it possible for anyone to use advanced graph techniques with Neo4j.
Zach Blumenfeld is a graph enthusiast who helps data scientists, engineers, and business leaders understand and implement Graph Analytics to solve challenging business problems. He has first hand experience with a wide range of modern day analytical challenges, including criminal fraud detection, identity resolution, and recommendation systems. Serving in both data science and software developer capacities, Zach has applied graph computing for law enforcement and government entities in support of missions that counter drug trafficking, human smuggling, money laundering, and child exploitation. He has led the development and deployment of full stack graph systems designed to facilitate broad search and analytical query requirements. Zach is excited to have recently joined Neo4j as Data Science Product Specialist, where he will help empower the field with Neo4j’s industry leading Graph Data Science (GDS) capabilities.
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