Improve ML Predictions using Connected Feature Extraction
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The most practical way to improve our machine learning predictions right away is using graph algorithms for connected feature extraction. We’ll quickly dive into creating a machine learning pipeline and tips on training and evaluating a model for link prediction – integrating Neo4j and Spark in our workflow. We’ll look at an example using several models to predict future collaborations and show measurable improvements using graph based features. Speaker: Amy Hodler
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Improve ML Predictions using Connected Feature Extraction
- 1. WIFI SSID:SparkAISummit | Password: UnifiedAnalytics
- 2. Amy Hodler, Neo4j Improve ML Predictions using Connected Features #Neo4j #GraphAnalytics #UnifiedAnalytics #SparkAISummit
- 3. The Next 20 Minutes • Graphs for Predictions • Link Prediction • Neo4j + Spark Workflow #UnifiedAnalytics #SparkAISummit #Neo4j #GraphAnalytics Amy E. Hodler Graph Analytics & AI Program Manager, Neo4j Amy.Hodler@neo4j.com @amyhodler neo4j.com/ graph-algorithms-book Chapter 8: Link Prediction Spark & Neo4j
- 4. What in Common is Predictive?
- 5. Relationships Are often the Strongest Predictors of Behavior “Increasingly we're learning that you can make better predictions about people by getting all the information from their friends and their friends’ friends than you can from the information you have about the person themselves”
- 7. Graph Data Science Use Cases
- 8. ML How Graph Technology is Changing AI 4:30 PM Room 2002
- 10. Features for ML: Feature Extraction Feature Extraction is how when we change the shape or format of the data to be usable in a machine learning pipeline. For example, from a graph, we extract the relevant subset of the data into a tabular format for model building.
- 11. Features for ML: Feature Engineering Feature Engineering is how we combine and process the data to create new, more meaningful features, such as clustering or connectivity metrics. Influence Connectivity Communities Relationships
- 12. Features for ML: Feature Selection Feature Selection is how we reduce the number of features used in a model to a relevant subset. This can be done algorithmically or based on domain expertise, but the objective is to maximize the predictive power of your model while minimizing overfitting.
- 13. Stop Throwing Away Data You Already Have Decisions $ Better Decisions Machine Learning Pipeline Machine Learning Pipeline
- 14. Link Prediction
- 15. Can we infer which new interactions are likely to occur in the future?
- 16. #UnifiedAnalytics #SparkAISummit + 50 years of biomedical data integrated in a knowledge graph Predicting new uses for drugs by using the graph structure to create features for link prediction 16 het.io
- 18. Link Prediction Methods Algorithm Measures Run targeted algorithms and score outcomes Set a threshold value used to predict a link between nodes Machine Learning Use the measures as features to train an ML model Community Detection Link Prediction Similarity 1st Node 2nd Node Common Neighbors Preferential Attachment label 1 2 4 15 1 3 4 7 12 1 5 6 1 1 0
- 20. Predicting Collaboration with a Graph Enhanced ML Model • Citation Network Dataset - Research Dataset – Used a subset with 52K papers, 80K authors, 140K author relationships and 29K citation relationships – “ArnetMiner: Extraction and Mining of Academic Social Networks”, by J. Tang et al • Neo4j – Create a co-authorship graph and connected feature engineering • Spark and MLlib – Train and test our model using a random forest classifier
- 21. Our Link Prediction Workflow Import Data Create Co-Author Graph Extract Data & Store as Graph Explore, Clean, Modify Prepare for Machine Learning Train Models Evaluate Results Productionize Identified sparse feature areas Feature Engineering: New graphy features Train / Test Split Resample: Downsampled for proportional representation Precision, Accuracy, Recall ROC Curve & AUC Model Selection: Random Forest Ensemble method
- 22. Graph Algorithms Used for Feature Engineering (few examples) Preferential Attachment measure the closeness of nodes based on shared neighbors Common Neighbors measures the number of possible neighbors (triadic closure) Illustration from be.amazd.com/link-prediction/
- 23. Triangle counting and clustering coefficients measure the density of connections around nodes Louvain Modularity identifies interacting communities and hierarchies Graph Algorithms Used for Feature Engineering (few examples)
- 24. Training Our Model This is one decision tree in our Random Forest used as a binary classifier to learn how to classify a pair: predicting either linked or not linked.
- 25. OMG I’m Good! Data Leakage! We had to go back and use time- based splits for train/test datasets Did you get really high accuracy on your first run without tuning?
- 28. Feature Influence for Tuning To compute feature importance, the random forest algorithm in Spark averages the reduction in impurity across all trees in the forest Feature rankings are in comparison to the group of features evaluated
- 29. Resources #UnifiedAnalytics #SparkAISummit #Neo4j #GraphAnalytics Code/Repositories: This example from O’Reilly book bit.ly/2FPgGVV (ML Folder) Python notebook: github.com/AliciaFrame/ Public-Python-Notebooks neo4j.com/ graph-algorithms-book Chapter 8: Link Prediction
- 30. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT Amy.Hodler@neo4j.com
- 31. Extra for Q&A #UnifiedAnalytics #SparkAISummit #Neo4j #GraphAnalytics
- 32. Resources Spark Community • spark.apache.org/community.html • users@spark.apache.org #UnifiedAnalytics #SparkAISummit #Neo4j #GraphAnalytics Code/Repositories This example from O’Reilly Book: bit.ly/2FPgGVV (ML Folder) Python notebook: github.com/AliciaFrame/ Public-Python-Notebooks Neo4j Community • neo4j.com/developer/ • neo4j.com/developer/graph-algorithms/ • community.neo4j.com
- 33. CAR DRIVES name: “Dan” born: May 29, 1970 twitter: “@dan” name: “Ann” born: Dec 5, 1975 since: Jan 10, 2011 brand: “Volvo” model: “V70” Latitude: 37.5629900° Longitude: -122.3255300° Nodes • Can have Labels to classify nodes • Labels have native indexes Relationships • Relate nodes by type and direction Properties • Attributes of Nodes & Relationships • Stored as Name/Value pairs • Can have indexes and composite indexes • Visibility security by user/role Neo4j Invented the Labeled Property Graph Model MARRIED TO LIVES WITH OW NS PERSON PERSON 33
- 34. ML Model - Random Forest
- 36. neo4j.com/graph-algorithms-book Free O’Reilly Book Spark and Neo4j Examples Chapter 8: Machine Learning Visit the Neo4j Booth