Hi ,
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 the
latest episode of Value Driven Data Science, Dr Alessandro Negro, Chief Scientist at GraphAware and author of Graph-Powered Machine Learning and Knowledge Graphs Applied, joins me to discuss how data scientists can exploit the natural relationships that exist within network datasets through the use of graph-powered machine learning.
Some of the things we discuss in this episode include:
- 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.
You can listen to this episode by clicking the button below, or find it on Apple Podcasts, Amazon Music, Spotify or Google Podcasts.
