Neo4j MeetUp - Graph Exploration with MetaExp

Graph Exploration w/ Neo4j
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https://s3.amazonaws.com/dev.assets.neo4j.com/wp-content/uploads/graph-data-technologies-graph-databases-for-beginners.png

3GRAPH EXPLORATION
Efficiently extracting knowledge from graph data
even if we do not know exactly what we are looking for
Graph Exploration: From Users to Large Graphs.
CIKM 2016, SIGMOD 2017, KDD 2018

Adaptive Databases
GRAPH EXPLORATION
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Graph Exploration Stack
Intuitive queries
Interactive algorithms
Users
Graph

Adaptive Databases
Intuitive queries
Interactive algorithms
GRAPH EXPLORATION
5
Graph Exploration Stack
Users
Graph
This project is about ...

6
3,27 × 109
base pairs
Graph Exploration in Biology - Complex Graphs
http://jcs.biologists.org/content/joces/118/21/4947/F3.large.jpg

Graph Exploration in Biology - Status Quo
MATCH (p1:Phenotype)-[:HAS]-(a1:Association)-[:HAS]-(snp:Snp)-[:HAS]-(a2:Association)-[:HAS]-(p2:Phenotype)
WHERE p1.name = 'foo1‘ AND p2.name = 'foo2p‘ AND a1.p < 0.01 AND a2.p < 0.01
WITH DISTINCT snpORDER BY snp.sid
RETURN collect(snp.sid)
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MATCH
(p1:Phenotype)-[:HAS]-(a1:Association)-[:HAS]-(snp:Snp)-[:HAS]-(a2:Association)-[:HAS]-(p2:Phenotype
)
WHERE p1.name = 'foo1'
AND p2.name = ‘foo2'
AND a1.p < 0.01 AND a2.p < 0.01
WITH DISTINCT snp
MATCH (snp)-[:IN]-(pw:PositionWindow)<-[:IN]-(l:Locus)--(g:Gene)
WHERE l.feature= 'gene'
RETURN collect(DISTINCT g.name)
MATCH
(p1:Phenotype)-[:HAS]-(a1:Association)-[:HAS]-(snp:Snp)-[:HAS]-(a2:Association)-[:HAS]-(p2:Phenotype)
WHEREp1.name = 'foo1'
ANDp2.name = ‘foo2'
ANDa1.p < 0.01 AND a2.p < 0.01
WITH DISTINCT snp
MATCH(snp)-[:IN]-(pw:PositionWindow)<-[:IN]-(l:Locus)--(g:Gene)
WITH DISTINCT g ORDER BY g.name
MATCH(g)-[:CODES]-(:Transcript)-[:CODES]-(p:Protein)-[:MEMBER]-(go:Goterm)
WHERE go.namespace= 'biological_process'
WITH DISTINCT go,p
RETURN go.name, count(p) ORDER BY count(p)DESC
LIMIT 10
MATCH
(p1:Phenotype)-[:HAS]-(a1:Association)-[:HAS]-(snp:Snp)-[:HAS]-(a2:Association)-[:HAS]-(p2:Phenotype)
WHERE p1.name = 'foo1'
AND p2.name = ‘foo2'AND a1.p < 0.01 AND a2.p < 0.01
WITH DISTINCT snpMATCH (snp)-[:IN]-(pw:PositionWindow)<-[:IN]-(l:Locus)--(g:Gene)
WITH DISTINCT gORDER BY g.name
MATCH (g)-[:CODES]-(:Transcript)-[:IS]-(ps:Probeset)-[:SIG]-(s:Sample)
WHERE s.name= 'mustafavi'
RETURN DISTINCT g.name

Problem
● Given two node sets:
How similar are they in my understanding?
● Example
→ Set of movies I like
→ Set of movies I don’t know
→ Will I like the movies I don’t know?
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As
Good
As It
Gets
Hell or
High
Water
Pulp
Fiction
The
Matrix
Skyfall
Avatar
?

What is a Knowledge Graph?
● (directed) graph G : ⟨V, E, φ, ψ⟩, where
○ V is a set of nodes,
○ E ⊆ V × V is a set of edges,
○ φ : V → LV
is an edge labeling function
and
○ ψ : E → LE
is a node labeling function
We refer to the elements of LV
and LE
as node
labels and edge labels
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What are Meta-Paths?
Graph Path
Graph Schema Meta-Path
A meta-path for a path ⟨n1
, ..., nt
⟩, ni
∈ V , 1 ≤ i ≤ t is a sequence
P : ⟨φ(n1
),ψ (n1
, n2
), ..., ψ (nt−1
, nt
), φ(nt
)⟩ that alternates node- and
edge-types along the path.
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Motivating Example
Q: How famous is Diane Kruger in America?
MATCH(n:Person)
WHERE n.name = “Diane Kruger”
RETURN n
MATCH(m:Movie)
WHERE m.location = “America”
RETURN m
Diane
Kruger
As
Good
As It
Gets
Stand
By MeTop
Gun
Pulp
Fiction A Few
Good
Men
The
Matrix
Up
12

How similar are they?
● Similarity depends on
○ expert knowledge
○ connections among nodes
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Individualized
exploration
Extract ratingsCompute Meta-Paths
What does the System do and how?
Overview
✓✗
◎
Learn representation
for meta-paths
Calculate
similarity
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Approximate Meta-Paths
Problem: How to compute all meta-paths fast?
Approx. Solution: Mine meta-paths using the graph’s schema!
Meta-Paths Computation
Compute schema
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Approximate Meta-Paths
Problem: How to compute all meta-paths fast?
Approx. Solution: Mine meta-paths using the graph’s schema!
Meta-Path
approximation
• Extract real and some
non-existent
meta-paths via DFS
on schema
Classifier
• Real meta-paths as
training data
• Predict probability of
meta-path
Exhaustive Meta-Path
computation
• Extract real
meta-paths via DFS
• Early termination
Training data Inference data
Neo4j Graph Algorithm Procedures
Use procedures for schema and meta-paths computation
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Learning a Meta-Path Embedding
Problem: Vector representation required for active
learning and preference prediction.
Meta-Paths Embedding
?
(3 5 1)T
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Learning a Meta-Path Embedding
Problem: Vector representation required for active
learning and preference prediction.
Solution: Embed meta-paths
→ Similar meta-paths should have similar vectors.
Challenges:
- Multiple similarity interpretations for meta-paths
- Large number of meta-paths.
- Shared parts between meta-paths.
Our method: Modified word2vec (and paragraph
vectors) method.
Meta-Paths Embedding
(3 5 1)T
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Learn the Domain Value of all Meta-Paths
- Problem: Users don’t want to rate all meta-paths
→ too many
→ time-consuming
→ tedious and boring
- Solution: Label only a few, but very informative paths
→ explore the space of all meta-paths
→ sequentially query the most uncertain datapoint
⇒ use active learning strategy
Active Learning
✓✗
◎
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Use Learned Preferences for Graph Exploration
Result Explanation
Icons made by Eucalyp from www.flaticon.com is
licensed by CC 3.0 BY
Graph (with
meta-paths)
Domain Knowledge
What is
important in
the graph?
Personalized Exploration
Tool
Similarity Measure
Related Nodes
Stats
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Personalized Exploration Tool
Result Explanation
Transform Nodes to
Vectors
(Graph-Embedding)
Adapt Vectors Using
Domain-Knowledge
Personalized Vector
Space
precomputed
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Result Explanation
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Problem: Suggest similar nodes with regard to given node sets and
user preferences.
Solution: Learn personalized similarity metric between nodes →
Similar nodes should have similar vector space embeddings.
Challenges:
- Different view of abstraction between meta-path preferences and
node instances
- High computational costs but online adaption of embedding space
needed
Our method: Modified node2vec through bias on already
pretrained embeddings

Result Explanation
What nodes
are close to
my selection?How close are
my sets?
Find clusters!
What are
outliers?Personalized Vector
Space
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System Architecture - How does it work with Neo4j?
Neo4j Graph Database
Neo4j Graph Algorithm Procedures
Containing Meta-Paths Computation
Python Backend Server
ReactJS Frontend
Meta-Path
Embedding
Active Learning Explanation
Node selection
Meta-Path
ordering
Result
visualization
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● Easy to get your code running in neo4j.
● Neo4j-graph-algorithms: efficiency vs convenience.
● Sometimes no stack-trace when an error occurs.
● Great support and community. Always available.
● Cypher: Easy to begin with, hard to master.
(hpi)-[:LIKES]->(neo4j)
What about neo4j?
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Outlook
4 research topics:
● Efficient meta-paths computation
● Sensible meta-paths embedding
● Precise active learning of meta-paths ranking
● Explanation and exploration using ranked meta-paths
Open Source!
● Meta-paths computation in neo4j graph algos
● MetaExp Repository
● You can use and improve as well!
https://hpi.de/mueller/metaexp/ 27

Neo4j MeetUp - Graph Exploration with MetaExp

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