Machine Learning and Link Prediction
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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, Mark 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. You will see how to fine-tune the elements you measure and understand the results for decisions or further adjustments. Learn how to exploit the power of connected data to improve prediction analysis!
YOU MAY ALSO LIKE:
- A NetworkX-esque API for Neo4j Graph Algorithms (SkillsCast recorded in August 2018)
- Real-time Systems with Spark Streaming and Kafka (in London on 30th September - 1st October 2019)
- Leonardo De Marchi's Deep Learning Fundamentals (in London on 22nd - 23rd October 2019)
- Scala eXchange London 2019 (in London on 12th - 13th December 2019)
- Keynote by Noelia Jiménez Martínez and Phil Harvey on Data: A Guide To Humans (in London on 16th September 2019)
- Design to Code: Meet Supernova! (in London on 16th September 2019)
- Neo4j (SkillsCast recorded in September 2019)
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Thanks to our sponsors
Machine Learning and Link Prediction
Mark Needham
Mark Needham is a graph advocate and Developer Relations Engineer at Neo4j.
Please log in to watch this conference skillscast.
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, Mark 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. You will see how to fine-tune the elements you measure and understand the results for decisions or further adjustments. Learn how to exploit the power of connected data to improve prediction analysis!
YOU MAY ALSO LIKE:
- A NetworkX-esque API for Neo4j Graph Algorithms (SkillsCast recorded in August 2018)
- Real-time Systems with Spark Streaming and Kafka (in London on 30th September - 1st October 2019)
- Leonardo De Marchi's Deep Learning Fundamentals (in London on 22nd - 23rd October 2019)
- Scala eXchange London 2019 (in London on 12th - 13th December 2019)
- Keynote by Noelia Jiménez Martínez and Phil Harvey on Data: A Guide To Humans (in London on 16th September 2019)
- Design to Code: Meet Supernova! (in London on 16th September 2019)
- Neo4j (SkillsCast recorded in September 2019)
- Saiku 4 (SkillsCast recorded in September 2019)
Thanks to our sponsors
Machine Learning and Link Prediction
Mark Needham
Mark Needham is a graph advocate and Developer Relations Engineer at Neo4j.