Relationships are highly predictive of behavior, yet most data science models overlook this information as it's difficult to extract network structure to use at scale in machine learning (ML).
With graphs, relationships are embedded in the data itself, making it practical to add these predictive capabilities to your existing practices. In this session, you’ll learn more about:
- Using graph-native ML to make break-through predictions
- Taking different approaches to graph feature engineering from queries and algorithms to embeddings
- How Neo4j has democratized graph-based ML techniques, leveraging everything from classical network science approaches to deep learning and graph convolutional neural networks
We’ll also walk through how to generate representations of your graph using graph embeddings, create ML models for link prediction or node classification, and apply these models to add missing information to an existing graph or incoming graph data.
Abstract & Bio
Relationships Matter: Using Connected Data for Better Machine Learning