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Intelligent Persistence
Kevin Van Gundy
Head of Enterprise Deployment Strategy
graph databases and machine intelligence
ABOUT ME
compliments and questions: kevin@neo4j.com
concerns: ./dev/null
• Basically Graph-Gandalf
We'll start with a sad story…
Why?
Jeff Bezos is the Genghis Khan of the internet,
and he's coming for you.
StructureData Storage
ON STAGE
BEHIND THE SCENE
`
"Customer Journey"
StructureData Storage
ON STAGE
BEHIND THE SCENE
`
"Customer Journey"
Micro Payments
Sharing Economy
Social Networks
Data is Growing
User Expectations
TECHNOLOGY
Convenient
Accurate
Fast
Problem Solver Extensions of Self
Intuitive
Intelligent
Learning
Apps / Cpu / Mobile
UX / UI
Bots / AI / VR
Data & Data structures
Connected Enterprises
Traditional Players
Today
time
Relative Advantage
Data Volume
Data Volume
Data Centric Companies
Traditional Players
Today
Value in Data = Competitive
Advantage
time = literally tomorrow
"Machine Learning is now key to long-term
business competitiveness"
- Vinod Khosla
DON'T MISS THE BUS
Let's Explore Artificial Intelligence
"Artificial Intelligence"
Breaking Down AI
Machine execution of
algorithms is valuable…
but not necessarily new
Has many dependencies, including observed behavior & human training
Breaking Down AI
Algorithms that learn & improve over time
New* and Valuable
*Static algorithms whose results improve as the data improves are not new
Breaking Down AI
Deep Learning at Scale: Forming Unseen Connections
Connection-centered intelligence
reveals context & causality
New and
Valuable
Actually: there’s nothing magical here.
It’s just about tracing the relationships.
Lots of terms for this…
e.g. “Inferencing”: Tying Remote Causes to Proximate Effects
Applications of Graphs in
“Computer-Based Decision Making”*:
+ “Graph-Assisted
Learning”
+ “Graph-Based
Algorithms”
+ + “Machine
Intelligence”
"How do I get from A to B fastest?"
"Based on these features, how important will this kb article be?"
"Oh look, I found Sarah Conner"
But…where are the graph databases?
Nearly all AI algorithms are Graph Algorithms
They sit inside of my ML black box,
the graphs inside probably don’t matter much 

in the application of AI
So GraphDBs Don't Matter?
Real-Time	Query	Performance	
Neo4j	Versus	Rela.onal	and	Other	NoSQL	Databases	
Connectedness	and	Size	of	Data	Set	
Response	Time	
0	to	2	hops	
0	to	3	degrees	
Thousands	of	connec;ons	
Tens	to	hundreds	of	hops	
Thousands	of	degrees	
Billions		of	connec;ons	
Rela;onal	and	
Other	NoSQL	
Databases	
Neo4j	
Neo4j	is		
1000x	faster	
Reduces	minutes		
to	milliseconds	
100s of Hops
1000s of Degrees
Billions of Connections
0 to 2 Hops
0 to 3 Degrees
Thousands of Connections
ResponseTime
t = O (1)
t =
O
(log(n))
Neo4j : Index-free adjacent traversals
JOIN
: Index Scans
What does Matter is Performance
value
value
value
start
ID
type 1
type 2
…
ID
Key 1 Key 2 … Key n
end type
Key 1 Key 2 … Key n
Node
Relationship
in:
out:
value
value
value
R 1
R 1
R 2
R 2
…
…
R n
R n
in:
out:
R 1
R 1
R 2
R 2
R 3
… R n
Grouped by Type
In cache, nodes hold references to all of its relationships, nodes
on the other hand are simple, only holding its properties.
The relationships for each node is grouped by RelationshipType to
allow fast traversal of a specific type.
All references are by ID and traversals do indirect lookup through
the cache
Graph Boosted AI in Practice
#1. Graph-Based Algorithms
+
Global Iterative Graph Algorithms
PageRank Community Detection
2016 Presidential Debate #3
Twitter Graph
2016 Presidential Debate #3
Twitter Graph - Minus Bots
Further reading: https://medium.com/@swainjo/election-2016-debate-three-on-twitter-4fc5723a3872
Transactional Graph Algorithms
Pattern Matching & Filtering
Yellowstone National Park Ecosystem
Step 1: Collect Known Influences
(Willow)-[:HABITAT_FOR]->(Lincoln’s Sparrow)
(Aspen)-[:FOOD_FOR]->(Beaver)
(Beaver Ponds)-[:HABITAT_FOR]->(Beaver)
(Deer)-[:BROWSE_ON]->(Cottonwood)
(Berry Shrubs)-[:FOOD_FOR]->(Bears)
…
Yellowstone National Park Ecosystem
Step 2: Reveal Known Influences a Graph
MATCH path = (:Animal {Entity:"Wolves"})-[*]->(:Landscape {Entity:"Rivers"})
WITH extract(node IN nodes(path) | node.Yellowstone) AS factor, rand() AS number
RETURN factor AS How_Wolves_Affect_RiverStability
ORDER BY number
LIMIT 5
Yellowstone National Park Ecosystem
Step 3: Query 

Trophic Cascades Example
Conclusion:
Interns building models to predict 15 years 

worth of ecology in an afternoon!
#2. Graph-Assisted Learning
+
Graph-Assisted Learning:
Graph-Based Feature Extraction
Further reading: https://neo4j.com/blog/machine-learning-graphs-fake-news-epidemic-part-2/
Smarter Feature Extraction:
the input of your ML algorithm is derived from a graph query
MATCH p = (a1:Article {title: 'The Fake
News Epidemic'}) -[MENTIONS]-
>(n:Topic:Entity)<-[MENTIONS]-(a2:Article)
WITH count(p) AS commonality, a2.article_id
WHERE commonality >=2 RETURN a2
Graph-Assisted Learning:
Knowledge Graphs
Further listening: 1a16z Podcast: The Taxonomy of Collective Knowledge
“A lot of the things called AI are just fancy ontologies1”
#3. Machine Intelligence
+ +
Machine Intelligence:
eBay Shopbot: Conversational Commerce
Further reading: https://medium.com/@rjpittman/cracking-the-code-on-conversational-commerce-775b5172f312
Medium Post by RJ Pittman: Cracking the Code on Conversational Commerce
Why am I not living in a
Graph-Powered Utopia?
Skynet is Coming, Be Ready
• The algorithms are olds news, machine learning, for the most part, is an "understood"
field
• Machine Intelligence is a real-time problem, systems need to advise at the moment
decisions are being made (or make a decision autonomously)
• The key bottleneck to usable machine intelligence is performance
• Hardware performance: compute capability and accessibility of data
• Software performance: efficient algorithms and actionable insight
Why This Time is Different
• Faster, Cheap, Compute:
• Cloud Compute
• Programmable CPUs, GPU, FPGA, etc.
• Fast, Cheap, Storage:
• Flash and SSDs are CHEAP
• Big RAM is eating Big Data
• NoSQL Database Market and Tool Kits have Matured
• AI is Learning; Learning is Storage
• Have you heard of Python?
Caveat: Machine Intelligence Will Held-back by Legacy
Graph Boosted Artificial Intelligence
Knowledge Graphs
Provide Rich 

Context for AI
AI Visibility
Human-Friendly 

Graph Visualization
Graph Enhanced AI Models
Faster, More 

Accurate Development
Graph Execution of AI
Operationalize Real-Time OLAP
and Monitoring
Graph Analytics
Enrich AI Inputs with 

Graph Algorithms
Graph System of Record
Maintain a Source of 

Connected AI Truth
DON'T MISS THE BUS
Questions?

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