The document discusses how graphs can enhance AI and machine learning by providing structured connectivity data and features derived from graph algorithms, embeddings, and neural networks. It outlines steps for doing graph data science, including building knowledge graphs, developing graph-based features, and using graph neural networks. The document also provides examples of applying these graph techniques across domains like financial services, healthcare, and recommendations.
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How Graphs Enhance AI
1. Amy E. Hodler
Graph Analytics & AI Program Manager, Neo4j
Amy.Hodler@neo4j.com @amyhodler
How Graphs Enhance AI
Graphs in Data Science
Advancing through the Steps of
Graph Data Science
5. Financial Crimes Drug Discovery Recommendations
Cybersecurity Predictive Maintenance
Customer Segmentation
Churn Prediction Search/MDM
Graphs Data Science Applications
6. “The idea is that graph networks are bigger than
any one machine-learning approach.
Graphs bring an ability to generalize about
structure that the individual neural nets don't have.”
"Where do the graphs
come from that
graph networks
operate over?”
8. Building a Graph ML Model
Data
Sources
Native Graph
Platform
Machine
Learning
Aggregate Disparate
Data and Cleanse
Build Predictive
Models
Unify Graphs and
Engineer Features
Parquet JSON
and more…
MLlib
and more…
9. Spark Graph Native Graph
Platform
Machine Learning
Example: Spark & Neo4j Workflow
Graph
Transactions
Graph
Analytics
Cypher 9 in Spark 3.0
to create non-
persistent graphs
MLlib to Train Models
Native Graph Algorithms,
Processing, and Storage
Morpheus
integration
10. Explore Graphs Build Graph Solutions
• Massively scalable
• Powerful data pipelining
• Robust ML Libraries
• Non-persistent, non-native graphs
• Persistent, dynamic graphs
• Graph native query and algorithm
performance
• Constantly growing list of graph
algorithms and embeddings
12. Steps Forward in Graph Data Science
Graph Persistence
Knowledge
Graphs
Connected Feature
Engineering
Graph Native
Learning
13. Steps Forward in Graph Data Science
Query Based
Knowledge
Graph
Query Based
Feature
Engineering
Graph
Algorithm
Feature
Engineering
Graph
Embeddings
Graph Neural
Networks
Enterprise Delivery
DataScienceComplexity
Knowledge
Graphs
Graph Feature
Engineering
Graph Native
Learning
Graph Persistence
14. Steps Forward in Graph Data Science
Query Based
Knowledge
Graph
Query Based
Feature
Engineering
Graph Algorithm
Feature
Engineering
Graph
Embeddings
Graph Neural
Networks
Enterprise Maturity
DataScienceComplexity
15. Query-Based Knowledge Graphs
Connecting the Dots
• Multiple graph layers of financial
information
• Includes corporate data with
cross-relationships, external
news, and customized weighting
• Dashboards and tools
• Credit risk
• Investment risk
• Portfolio news recommendations
16. Steps Forward in Graph Data Science
Query Based
Knowledge
Graph
Graph
Algorithm
Feature
Engineering
Graph
Embeddings
Graph Neural
Networks
Query Based
Feature
Engineering
Enterprise Maturity
DataScienceComplexity
17. HetioNet is a knowledge
graph integrating over 50
years of biomedical data
Leveraged to predict new
uses for drugs by using the
graph topology to create
features to predict new links
Query-Based Feature Engineering
Mining Data for Drug Discovery
het.io
18. Query-Based Feature Engineering
Mining Data for Drug Discovery
HetioNet is a knowledge
graph integrating over 50
years of biomedical data
Leveraged to predict new
uses for drugs by using the
graph topology to create
features to predict new links
het.io
20. Spark Graph Native Graph
Platform
Machine Learning
• Merge distributed data
into DataFrames
• Reshape your tables
into graphs
• Explore cypher queries
• Move to Neo4j to build
expert queries
• Persist your graph
Knowledge Graphs:
Getting Started Example with Spark
• Bring query based
graph features to ML
pipeline
Graph
Transactions
Graph
Analytics
21. Steps Forward in Graph Data Science
Query Based
Feature
Engineering
Graph
Embeddings
Graph Neural
Networks
Query Based
Knowledge
Graph
Graph
Algorithm
Feature
Engineering
Enterprise Maturity
DataScienceComplexity
22. Feature Engineering is how we combine and process the
data to create new, more meaningful features, such as
clustering or connectivity metrics.
Graph Connected Feature Engineering
Add More Descriptive Features:
- Influence
- Relationships
- Communities
Extraction
23. 23
Graph Feature Categories & Algorithms
Pathfinding
& Search
Finds the optimal paths or evaluates
route availability and quality
Centrality /
Importance
Determines the importance of
distinct nodes in the network
Community
Detection
Detects group clustering or
partition options
Heuristic
Link Prediction
Estimates the likelihood of nodes
forming a relationship
Evaluates how alike
nodes are
Similarity Embeddings
Learned representations
of connectivity or topology
24. • Connected components to identify
disjointed graphs sharing identifiers
• PageRank to measure influence and
transaction volumes
• Louvain to identify communities
that frequently interact
• Jaccard to measure account
similarity
24
Graph Connected Feature Engineering
Financial Crime: Detecting Fraud
Large financial institutions already have existing pipelines to identify
fraud via heuristics and models
Graph based features improve accuracy:
26. Spark Graph Native Graph
Platform
Machine Learning
• Merge distributed data
into DataFrames
• Reshape your tables
into graphs
• Explore cypher queries
and simple algorithms
• Persist your graph
• Create rule based
features
• Run native graph
algorithms and write to
graph or stream
Graph Feature Engineering:
Getting Started Example with Spark
• Bring graph features
to ML pipeline for
training
Graph
Transactions
Graph
Analytics
27. 27
Graph Algorithms in Neo4J
• Parallel Breadth First Search
• Parallel Depth First Search
• Shortest Path
• Single-Source Shortest Path
• All Pairs Shortest Path
• Minimum Spanning Tree
• A* Shortest Path
• Yen’s K Shortest Path
• K-Spanning Tree (MST)
• Random Walk
• Degree Centrality
• Closeness Centrality
• CC Variations: Harmonic, Dangalchev,
Wasserman & Faust
• Betweenness Centrality
• Approximate Betweenness Centrality
• PageRank
• Personalized PageRank
• ArticleRank
• Eigenvector Centrality
• Triangle Count
• Clustering Coefficients
• Connected Components (Union Find)
• Strongly Connected Components
• Label Propagation
• Louvain Modularity – 1 Step & Multi-Step
• Balanced Triad (identification)
• Euclidean Distance
• Cosine Similarity
• Jaccard Similarity
• Overlap Similarity
• Pearson Similarity
Pathfinding
& Search
Centrality /
Importance
Community
Detection
Similarity
neo4j.com/docs/
graph-algorithms/current/
Link
Prediction
• Adamic Adar
• Common Neighbors
• Preferential Attachment
• Resource Allocations
• Same Community
• Total Neighbors
28. Steps Forward in Graph Data Science
Query Based
Knowledge
Graph
Graph
Algorithm
Feature
Engineering
Graph Neural
Networks
Query Based
Feature
Engineering
Graph
Embeddings
Enterprise Maturity
DataScienceComplexity
29. Embedding transforms graphs into a feature vector, or
set of vectors, describing topology, connectivity, or
attributes of nodes and edges in the graph
29
Graph Embeddings
• Vertex/Node embeddings: describe connectivity of each node
• Path embeddings: traversals across the graph
• Graph embeddings: encode an entire graph into a single vector
31. 31
Graph Embeddings - Recommendations
Explainable Reasoning over Knowledge Graphs for
Recommendation
32. Spark Graph Native Graph
Platform
Machine Learning
• Merge distributed data
into DataFrames
• Reshape your tables
into graphs
• Explore cypher queries
and simple algorithms
• Move to Neo4j to build
expert queries
• Write to persist
• Stay tuned for
DeepWalk and DeepGL
algorithms
Graph Feature Engineering-Embedding:
Getting Started Example with Spark
• Bring graph features
to ML pipeline for
training
Graph
Transactions
Graph
Analytics
33. Steps Forward in Graph Data Science
Query Based
Knowledge
Graph
Graph
Algorithm
Feature
Engineering
Query Based
Feature
Engineering
Graph Neural
Networks
Graph
Embeddings
Enterprise Maturity
DataScienceComplexity
34. Deep Learning refers to training multi-layer neural
networks using gradient descent
34
Graph Native Learning
35. Graph Native Learning refers to deep learning models
that take a graph as an input, performs computations,
and return a graph
35
Graph Native Learning
Battaglia et al, 2018
36. Example: electron path prediction
Bradshaw et al, 2019
36
Graph Native Learning
Given reactants and reagents, what will the
products be?
Given reactants and reagents, what will the
products be?
37. Steps Forward in Graph Data Science
Query Based
Knowledge
Graph
Query Based
Feature
Engineering
Graph
Algorithm
Feature
Engineering
Graph
Embeddings
Graph Neural
Networks
Enterprise Delivery
DataScienceComplexity
Knowledge
Graphs
Graph Feature
Engineering
Graph Native
Learning
Graph Persistence