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!
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- A NetworkX-esque API for Neo4j Graph Algorithms (SkillsCast recorded in August 2018)
- Elasticsearch for Developers with Itamar Syn-Hershko (in London on 23rd - 25th September 2019)
- Real-time Systems with Spark Streaming and Kafka (in London on 30th September - 1st October 2019)
- ProgNET London 2019 (in London on 11th - 13th September 2019)
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- Redis Streams + Spark Structured Streaming: Scale-out Your Continuous Apps (in London on 31st July 2019)
- London #PowerBI user group with Will Thompson (in London on 19th August 2019)
- How real-time data processing is used for application in customer experience? (SkillsCast recorded in July 2019)
- A Look at the technology of LUNO and Adjust (SkillsCast recorded in July 2019)
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)
- Elasticsearch for Developers with Itamar Syn-Hershko (in London on 23rd - 25th September 2019)
- Real-time Systems with Spark Streaming and Kafka (in London on 30th September - 1st October 2019)
- ProgNET London 2019 (in London on 11th - 13th September 2019)
- CloudNative London 2019 (in London on 25th - 27th September 2019)
- Redis Streams + Spark Structured Streaming: Scale-out Your Continuous Apps (in London on 31st July 2019)
- London #PowerBI user group with Will Thompson (in London on 19th August 2019)
- How real-time data processing is used for application in customer experience? (SkillsCast recorded in July 2019)
- A Look at the technology of LUNO and Adjust (SkillsCast recorded in July 2019)
Thanks to our sponsors
Machine Learning and Link Prediction
Mark Needham
Mark Needham is a graph advocate and Developer Relations Engineer at Neo4j.