How Graph Data Science can turbocharge your Knowledge Graph

© 2022 Neo4j, Inc. All rights reserved.
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How Graph Data Science can
turbocharge your Knowledge
Graph
Kristof Neys,
Director Graph Data Science Technology,
Neo4j Field Engineering
September 2022

Neo4j, Inc. All rights reserved 2021
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20/25
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Top Retail Firms
Top Financial Firms
Top Software Vendors
Anyway You Like It
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Creator of the Property
Graph and Cypher language
at the core of the GQL ISO
project. Fully integrated Data
Science Library
Thousands of Customers
World-Wide
HQ in Silicon Valley, offices
include London, Munich,
Paris & Malmo
Industry Leaders use Neo4j
On-Prem
DB-as-a-Service
In the Cloud

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Topics
We will cover:
1) Knowledge Graphs
2) Graph Data Science
3) Graph Embeddings
4) Knowledge Graphs + Graph Data Science
5) How to get started on your graph journey…

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Knowledge Graphs

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Why Knowledge Graphs?
Christopher Strachey in letter to Alan Turing:
"I am convinced that the crux of the problem of learning is
recognizing relationships and being able to use them"

What are Knowledge Graphs?
● Entities can be real-world objects and abstract concepts
● Relationships represent the connections between entities
● Semantic description of the entities and relationships
A knowledge graph is a structured representation of
facts, consisting of entities, relationships and semantic
descriptions

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Show me…! From Data points to Knowledge Graph
Car
name: “Dan”
born: May 29, 1978
twitter: “@dan”
name: “Ann”
born: Dec 5, 1979
since:
Jan 10, 2021
brand: “Volvo”
model: “V90”
LOVES
LOVES
LIVES_WITH
Person Person

User
User
IPLocation
IPLocation
Website
Website
Graphs allow you to make implicit
relationships….
….explicit
And they grow too…?!

User
User
IPLocation
IPLocation
Website
Website
User
PersonId: 1
PersonId: 1 PersonId: 1
User
PersonId: 2
…and can then group similar nodes…and
create a new graph from the explicit
relationships…
A graph grows organically - gaining
insights and enriching your data
Graphs Grow….!

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Knowledge graphs in Credit risk analysis

…and now becoming ubiquitous…

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• Challenge: Focus on preventative
maintenance to avoid costly post-failure
remedial actions
• Solution: 27 million warranty & service
documents parsed for text to knowledge
graph that is context for AI to learn “prime
examples” and anticipate maintenance
• Results:
○ Proactive remedial action has
saved downtime & associated
costs and increased productivity
Caterpillar
Preventative Maintenance

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We eat, sleep, drink..
Knowledge
Graphs…
And..
…We even
published a book-let
on it….get your free
copy.

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Neo4j Graph Data Science ~
what’s in the box….?

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From implicit to explicit…
Query your Knowledge
Graph
Graph Algorithms
Graph Native
Machine Learning
Find the patterns you’re
looking for in connected data
Use unsupervised machine
learning techniques to
identify associations,
anomalies, and trends.
Use embeddings to learn the
features in your graph that
you don’t even know are
important yet.
Train in-graph supervised ML
models to predict links,
labels, and missing data.

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65+ Graph Algorithms - Out of the Box
Pathfinding & Search Centrality Community Detection
❏ Delta-Stepping Single-Source
❏ Dijkstra’s Single-Source
❏ Dijkstra Source-Target
❏ All Pairs Shortest Path
❏ A* Shortest Path
❏ Yen’s K Shortest Path
❏ Minimum Weight Spanning Tree
❏ Random Walk
❏ Breadth & Depth First Search
❏ Degree Centrality
❏ Closeness Centrality
❏ Harmonic Centrality
❏ Betweenness Centrality & Approx.
❏ PageRank
❏ Personalized PageRank
❏ ArticleRank
❏ Eigenvector Centrality
❏ Hyperlink Induced Topic Search (HITS)
❏ Influence Maximization (Greedy, CELF)
❏ Weakly Connected Components
❏ Strongly Connected Components
❏ Label Propagation
❏ Leiden
❏ Louvain
❏ K-Means Clustering
❏ K-1 Coloring
❏ Modularity Optimization
❏ Speaker Listener Label Propagation
❏ Approximate Max K-Cut
❏ Triangle Count
❏ Local Clustering Coefficient
❏ Conductance Metric
Heuristic LP Similarity Graph Embeddings
❏ Adamic Adar
❏ Common Neighbors
❏ Preferential Attachment
❏ Resource Allocations
❏ Same Community
❏ Total Neighbors
❏ K-Nearest Neighbors (KNN)
❏ Filtered K-Nearest Neighbors (KNN)
❏ Node Similarity
❏ Filtered Node Similarity
❏ Similarity Functions
❏ Fast Random Projection (FastRP)
❏ Node2Vec
❏ GraphSAGE

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Before we go any further…let’s
quiz!

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Which of the colored nodes would be considered the most
‘important'?

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Which of the colored nodes would be considered the most
‘important'?

Graph Embeddings:
From Chaos to Structure…
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Node Embedding
What are node embeddings?
How?
The representation of nodes as low-dimensional vectors that
summarize their graph position, the structure of their local graph
neighborhood as well as any possible node features
Encoder - Decoder Framework

Node Embedding

Graph Embeddings in Neo4j
Node2Vec
Random walk based embedding
that can encode structural similarity
or topological proximity.
Easy to understand, interpretable
parameters, plenty of examples
GraphSAGE
Inductive embedding that encodes
properties of neighboring nodes
when learning topology.
Generalizes to unseen graphs, first
method to incorporate properties
FastRP
A super fast linear algebra based
approach to embeddings that can
encode topology or properties.
75,000x faster than Node2Vec
extended to encode properties

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OK - we have vectors…
Now what?

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Machine Learning Pipelines
AutoML for in-graph machine learning:
● Node Classification
● Node Regression
● Link Prediction
ML pipelines support:
● Data splitting & rebalancing
● Feature engineering
● Model evaluation and selection
● Automated hyperparameter tuning
Trained models in the catalog
● Persistable
● Publishable
● Automatically applies pipelines
to new data for predictions

Graph Neural Networks are HOT!

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Knowledge Graphs + Graph Data Science
A marriage made in heaven…

Turbocharging your KG with Data Science….

Suppose…

Knowledge Graph completion
● Elon Musk....
● Human or Machine?
● ...born on Mars, Holiday...?

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One last thing….
It’s better with Transformers...

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Get started on your Graph Journey

https://github.com/Kristof-Neys

Resources
● Neo4j Sandbox:
○ https://neo4j.com/sandbox/
● Articles:
○ https://kristof-neys-58246.medium.com/
● Colab notebook:
○ https://colab.research.google.com/drive/15oBxD2zj64nDgaaIq5y2Upf2Dh1pA
mjn?usp=sharing#scrollTo=n-f1kzjTYiFc
● Contacts:
○ kristof.neys@neo4j.com

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Resources
Graph Resources
● Video: Advantages of Graph Technology
● Whitepaper: AI & Graph Technology: Enhancing AI with Context &
Connections
● Whitepaper: Financial Fraud Detection with Graph Data Science
● Case Study: Meredith Corporation
Neo4j BookShelf
● Graph Databases For Dummies
● Graph Data Science For Dummies
● O’Reilly Graph Algorithms

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Thank you!

How Graph Data Science can turbocharge your Knowledge Graph

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