ntroducing to the Power of Graph Technology

Neo4j, Inc. All rights reserved 2021
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Introducing to the Power of Graph
Technology
Kristof Neys,
Graph Data Science Specialist, Field Engineering EMEA/APAC
May 2022

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Why Graphs?

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Driving Intelligence into Data with Knowledge Graphs
Data Graph
Dynamic Context
Knowledge Graph
Deep Dynamic Context

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Graphs allows you to make implicit
relationships….
….explicit
Graphs….Grow!

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…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!

Not that long ago…. Deepmind stated...
“We argue that combinatorial
generalisation must be a top
priority for AI to achieve
human-like abilities, and that
structured representations [i.e.
Graphs] and computations are
key to realizing this objective”

Everything is a Graph...

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results from https://dimensions.ai, a
site that tracks research papers. The
search was for "graph neural
network" OR "graph convolutional"
OR "graph embedding" OR "graph
learning" OR "graph attention" OR
"graph kernel" OR "graph
completion"
Because I say so others say so!

Graph Neural Networks are HOT!

“By 2025, graph technologies will be
used in 80% of data and analytics
innovations...”
Top 10 Trends in Data and Analytics, 11 May 2020, Rita Sallam et al.

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What can Neo4j Graph Data Science do?

Neo4j’s Graph Data Science Framework
Neo4j Graph Data
Science Library
Neo4j
Database
Neo4j
Bloom
Scalable Graph Algorithms &
Analytics Workspace
Native Graph Creation &
Persistence
Visual Graph
Exploration & Prototyping

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Graphs & Data Science
Knowledge Graphs
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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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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60+ Graph Data Science Techniques in Neo4j
Pathfinding &
Search
• Shortest Path
• Single-Source Shortest Path
• All Pairs Shortest Path
• A* Shortest Path
• Yen’s K Shortest Path
• Minimum Weight Spanning Tree
• K-Spanning Tree (MST)
• Random Walk
• Breadth & Depth First Search
Centrality &
Importance
• Degree Centrality
• Closeness Centrality
• Harmonic Centrality
• Betweenness Centrality & Approx.
• PageRank
• Personalized PageRank
• ArticleRank
• Eigenvector Centrality
• Hyperlink Induced Topic Search (HITS)
• Influence Maximization (Greedy, CELF)
Community
Detection
• Triangle Count
• Local Clustering Coefficient
• Connected Components (Union Find)
• Strongly Connected Components
• Label Propagation
• Louvain Modularity
• K-1 Coloring
• Modularity Optimization
• Speaker Listener Label Propagation
Supervised
Machine Learning
• Node Classification
• Link Prediction
… and more!
Heuristic Link
Prediction
• Adamic Adar
• Common Neighbors
• Preferential Attachment
• Resource Allocations
• Same Community
• Total Neighbors
Similarity
• Node Similarity
• K-Nearest Neighbors (KNN)
• Jaccard Similarity
• Cosine Similarity
• Pearson Similarity
• Euclidean Distance
• Approximate Nearest Neighbors (ANN)
Graph
Embeddings
• Node2Vec
• FastRP
• FastRPExtended
• GraphSAGE
• Synthetic Graph Generation
• Scale Properties
• Collapse Paths
• One Hot Encoding
• Split Relationships
• Graph Export
• Pregel API (write your own algos)

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How can they be used?
Stand Alone Solution
Find signiﬁcant patterns and optimal
structures
Use community detection and
similarity scores for recommendations
Machine Learning Pipeline
Use the measures as features to train
an ML model
1st
node
2nd
node
Common
neighbors
Preferential
attachment
Label
1 2 4 15 1
3 4 7 12 1
5 6 1 1 0
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Our Implementations are Fast - and Getting Faster
LDBC100
(LDBC Social Network Scale Factor 100)
300M+ nodes
2B+ relationships
LDBC100PKP
(LDBC Social Network Scale Factor 100)
500k nodes
46M+ relationships
Logical Cores: 64
Memory: 512GB
Storage: 600GB
NVMe-SSD
AWS EC2 R5D16XLarge
Intel Xeon Platinum 8000
(Skylake-SP or Cascade Lake)
Node Similarity
20min
Betweenness Centrality
10min
Node2Vec
2.8min
Label Propagation
46sec
Weakly Connected
Components
36sec
Local Clustering
Coefficient
4.76min
FastRP
1.33min
PageRank
53sec
Louvain
14.66min

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It’s all about Embeddings…

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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Graph Machine Learning

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Node Classification - in Neo4j
Load your in- memory
graph with labels &
features
Use
nodeClassification.train
Specify the property you want to
predict and the features for making
that prediction
Node classification:
Predicting a node label or (categorical) property
Neo4j Automates the Tricky Parts:
1. Splits data for train & test
2. Builds logistic regression models using the training data
& specified parameters to predict the correct label
3. Evaluates the accuracy of the models using the test data
4. Returns the best performing model
The predictive model
appears in the model
catalog, ready
to apply to
new data

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Link Prediction - in Neo4j
Load your in- memory
graph with labels &
features
Use
linkPrediction.train
Split your graph into train & test
splitRelationships.mutate
Link Prediction:
Predicting unobserved edges or relationships that will form in the future
Neo4j Automates the Tricky Parts:
1. Builds logistic regression models using the training data
& specified parameters to predict the correct label
2. Evaluates the accuracy of the models using the test data
3. Returns the best performing model
The predictive model
appears in the model
catalog, ready
to apply to
new data

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Link prediction

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Link Prediction

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Validation of Graph Technology
in Banking

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Knowledge graphs in Credit risk analysis

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Stock Market sentiment

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Neo4j working with AWS
Sagemaker

Neo4j in the AWS Ecosystem
AWS Cloud
Kafka Connect
Plugin
Connector for Apache
Spark
Neo4j Graph Data
Science
Neo4j Graph
Database
Neo4j
Bloom
Database Business Intelligence
Analytics
Connector for BI
Amazon S3
Amazon SageMaker
Amazon Managed
Streaming for Apache Kafka
Amazon QuickSight
Amazon EMR

Neo4j and SageMaker
1. Generate graph feature embeddings in Neo4j
Graph Data Science
2. Export to S3
3. Import into SageMaker
4. Supervised Learning
AWS Cloud
Amazon SageMaker
Amazon S3

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Banking Fraud: A use case

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Accelerated Fraud Detection in FinTech
• As part of a larger business transformation initiative, they wanted to
reduce cost and time associated with money transfers
• False negatives in AML/Fraud create delays during investigation and
ultimately unhappy customers
• From established base in EU, rapid expansion into US and APAC meant
compliance to new regulations and laws
• Current home-grown solution was too slow (manual), expensive and didn’t
scale as fast as the business growth
• Traditional rules-based approach only focused on known issues while
fraudsters “think ahead”
The Challenge:
“We had been an Insights customer for years with Synthetics but needed to better
understand the real experience of our customers and how they were impacted by
changes to the site. Business Analytics with Insights solved that for us.”
● International B2B payment delivery and banking services provider
Founded in 2013, headquartered in Luxembourg with 200 customers
● Processing 6% of European B2C e-commerce payments in 2020 and
over 250 billion Euros in payments volumes
● Delivers rapid access to direct clearing and partner banks enabling
cross-border payments in 25 currencies
• Required an on-going, scalable and supported
solution as opposed to “throwing bodies at the
problem”
• Sought a forward-looking, Machine Learning
(ML) solution compatible with their AWS
architecture
• Insights customer for 10 years
• Dynatrace DEM (RUM + Synthetic) for 2 years
The Requirements:

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Reduced false negatives & alerts = ROI in months
“We had been an Insights customer for years with Synthetics but needed to better
understand the real experience of our customers and how they were impacted by
changes to the site. Business Analytics with Insights solved that for us.”
“For AML, when you visualize all the connections on a
screen, you can very easily spot important items: what used
to take 3+ days to look for a connection can be found in
less than 30 seconds.”
Ruben Menke - Sr Data Scientist, Banking Circle
• Reduced false negatives by 25%
• Decreased numbers of overall alerts escalated for manual reviews by 50%
• Empowered non-technical users (i.e. investigators) to gain instant insights
in graph form
• ML approach optimizes information from customers to see patterns and
build models based on real-time data
The Solution:

Neo4j and AWS
● >40% of Neo4j customers run on AWS
● Member of Amazon Partner Network since 2013
● APN Advanced Tier Partner
○ AWS ISV Workload Migration
○ APN Global Startup
○ ISV Accelerate
● Collaborative Joint Engineering

On the AWS Marketplace:
● Neo4j Enterprise
○ AMI and CFT
○ BYOL
○ Graph Database
○ Graph Data Science
○ Bloom
● AuraDB Enterprise
○ DBaaS
Getting Started

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Thank you!
● Try it yourself!
○ Neo4j-Partners GitHub
○ AWS Quick Start
● Further Reading
○ Landing Page
○ APN Partner Finder
● Contact us: ecosystem@neo4j.com
Q&A

ntroducing to the Power of Graph Technology

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