Join us to understand how you can use graph-native machine learning in Neo4j to make breakthrough predictions. Previously only accessible to researchers and a very few advanced tech companies, Neo4j has democratized graph-based ML techniques that leverage deep learning and graph convolutional neural networks.
Most data science models ignore network structure while graphs add highly predictive features to ML models, increasing accuracy and enabling otherwise unattainable predictions based on relationships. With the recent update to the Neo4j Graph Data Science library, anyone can take advantage of this state-of-the-science technique to create representations of your graph’s most significant features for new and more accurate predictions with the data you already have.
In this session, we’ll explain our new graph embeddings and demonstrate using the GraphSAGE embedding results with our new ML catalog. We’ll also visualize the predictions of different models using Neo4j Bloom.
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.
Amy Hodler is the Graph Analytics & AI program director at Neo4j. She loves seeing how the community uses graph analytics to reveal structures within real-world networks and infer behavior.
Amy is the co-author of the O'Reilly book Applied Graph Algorithms in Apache Spark and Neo4j, published in early 2019 and updated July 2020.
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