Devoxx UK 2019
from Wednesday 8 May to Friday 10 May 2019.
Mark Needham is a graph advocate and Developer Relations Engineer at Neo4j.
Mark helps users embrace graphs and Neo4j, building sophisticated solutions to challenging data problems. Mark has deep expertise in graph data having previously helped to build Neo4j’s Causal Clustering system.
Mark is a co-author of the book 'Graph Algorithms: Practical Examples in Apache Spark and Neo4j', due to be released in early 2019, and writes about his experiences of being a graphista on a popular blog at markhneedham.com. He tweets at @markhneedham.
See also https://markhneedham.com
Machine learning uses algorithms to train software through specific examples and progressive improvements based on expected outcome. However, traditional data structures can fail to detect behavior without the contextual information because they lack the strongest predictors of behavior - relationships. Just as humans require contextual information to make better decisions, so do machine-learning algorithms. Combining ML processing with a graph data structure can help fill in the missing contextual information and improve our predictions. In this session, we will show what graph has to offer and show an example applying link prediction analysis to estimate how likely academic authors are to collaborate with new co-authors in the future. We will see how to fine-tune the elements we measure and understand the results for decisions or further adjustments. Learn how to exploit the power of connected data to improve prediction analysis!