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Relationships Matter: Using Connected Data for Better Machine Learning
Relationships are highly predictive of behavior, yet most data science models overlook this information because it’s difficult to extract network structure for use 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.
That’s why we’re presenting and demoing the use of graph-native ML to make breakthrough predictions. This will cover:
• Different approaches to graph feature engineering, from queries and algorithms to embeddings • How ML techniques leverage everything from classical network science to deep learning and graph convolutional neural networks • 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/incoming data • Why no-code visualization and prototyping is important