From social media to electricity grids and the internet itself, we live in a highly interconnected world. But traditional data science techniques don’t adequately allow for the relationships that can exist between data points in such networks. This is where graph data analysis comes into play.
In this episode, Dr Alessandro Negro joins Dr Genevieve Hayes to discuss how data scientists can exploit the natural relationships that exist within network datasets through the use of graph-powered machine learning.
Guest Bio
Dr Alessandro Negro is the Chief Scientist at GraphAware, the world’s #1 Neo4j consultancy, and Managing Director at GraphAware Italy. He is also the author of Graph-Powered Machine Learning and the recently released Knowledge Graphs Applied.
Talking Points
- What is graph data and how does it differ from structured data?
- Use cases for graphs and graph databases?
- What is a knowledge graph, how are they created and what are their benefits?
- How can graphs be used to power machine learning?
- How can machine learning algorithms be used to build knowledge graphs?
- Steps data scientists can take to get started with graph data science and knowledge graphs.
Links
- Connect with Genevieve on LinkedIn
- Be among the first to hear about the release of each new podcast episode by signing up HERE
