From Data Science to AI via Graph Analytics

Nodes Room

Until recently, few people had much experience putting graphs to work — not beyond university homework based on Dijkstra’s algorithm or calculating centrality. Imagination filled gaps where direct experience was rare.

Today, however, graph applications are becoming more commonplace. They don’t require the enormous scale of social networks. Many interesting graph use cases fit within memory, obviating the need for databases.

While SPARQL has good applications, using graphs doesn’t imply the entirety of the Semantic Web. With deep learning, graph embedding models go further toward abstraction, recognizing patterns in context without the original graph data anywhere nearby.

In this panel, we’ll embark on a journey from data science to AI via graph analytics. Some of the questions we will address:

How can metadata help with data governance and lineage? Where does one look for metadata standards, and how can they be used?

What are some common types of graph analytics and algorithms, and what are they good for?

What are graph embeddings, and what are they good for? How does one get started using them in the real world?

Panel moderated by Paco Nathan

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