Abstract: 

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:
1. Using graph-native ML to make break-through predictions
2. Taking different approaches to graph feature engineering from queries and algorithms to embeddings
3. 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.

Bio: 

Phani is a Data Science Solution Architect at Neo4j. He is a computational scientist and holds a PhD in Nanotechnology and Computational Materials Science from Louisiana Tech University. After a decade of research in batteries and electrical energy storage in both industry and academia, he transitioned to a career in data science and machine learning and since worked with two early stage start-ups in AI/ML space and large organizations like American Airlines and Infosys as a data science consultant. Currently, he is with Neo4j helping prospects and customers get started with Graph Data Science.