Machine learning has traditionally revolved around creating models around data that is characterized by embeddings attributed to individual observations. However, this ignores a signal that could potentially be very strong: the relationships between data points. Network graphs provide great opportunities for identifying relationships that we may not even realize exist within our data. Further, a variety of methods exist to create embeddings of graphs that can enrich models and provide new insights.
In this talk we will look at some examples of common ML problems and demonstrate how they can take advantage of graph analytics and graph-based machine learning. We will also demonstrate how graph embeddings can be used to enhance existing ML pipelines
Dr. Clair Sullivan is currently a graph data science advocate at Neo4j, working to expand the community of data scientists and machine learning engineers using graphs to solve challenging problems. Previously she worked at GitHub as a machine learning engineer while maintaining adjunct assistant professor status at the University of Illinois. She has authored 4 book chapters, over 20 peer-reviewed papers, and more than 30 conference papers