QQG-7409 Machine learning and link prediction | Devoxx

Devoxx UK 2019
from Wednesday 8 May to Friday 10 May 2019.

   Machine learning and link prediction

Conference

Big Data & AI
Big Data & AI
Beginner & novice level

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!

graph   Machine Learning for Developers   algorithms   graph databases  
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Mark Needham
Mark Needham
From Neo4j

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.


Jennifer Reif
Jennifer Reif
From Neo4j

Jennifer Reif is a Developer Relations Engineer at Neo4j, conference speaker, blogger, and an avid developer and problem-solver. She holds a Master’s degree in Computer Management and Information Systems and has worked with large enterprises to organize and make sense of widespread data assets and leverage them for maximum business value. She has worked with a variety of commercial and open source tools and enjoys learning new technologies, sometimes on a daily basis! Her passion is finding ways to organize chaos and deliver software more effectively.


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