Knowledge Graphs are becoming mission-critical across many industries. More recently, we are witnessing the application of Graph Data Science to Knowledge Graphs, offering powerful outcomes. But how do we define Knowledge Graphs in industry and how can they be useful for your project? In this talk, we will illustrate the various methods and models of Graph Data Science being applied to Knowledge Graphs and how they allow you to find implicit relationships in your graph which are impossible to detect in any other way. You will learn how graph algorithms from PageRank to Embeddings drive ever deeper insights in your data.
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7/10
20/25
7/10
Top Retail Firms
Top Financial Firms
Top Software Vendors
Anyway You Like It
2
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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User
User
IPLocation
IPLocation
Website
Website
Graphs allow you to make implicit
relationships….
….explicit
And they grow too…?!
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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
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…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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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'?
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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
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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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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
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https://github.com/Kristof-Neys
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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!