Machine learning and link prediction by Mark Needham & Jennifer Reif

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!