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Neo4j, Inc. All rights reserved 2021
Neo4j, Inc. All rights reserved 2021
1
Einblicke ins Dickicht der
Parteiprogramme
Data Science im Unternehmen
Michael Hunger
Director Developer Relations Neo4j
dev.neo4j.com/ltw-sa21
Neo4j, Inc. All rights reserved 2021
Es ist Wahljahr #btw2021 - Und wir alle gehen hin!
Neo4j, Inc. All rights reserved 2021
Wahl-O-Mat®
- Parteiprogramme im Vergleich
Neo4j, Inc. All rights reserved 2021
Ziel: Visuelle und Algorithmische Analyse
Neo4j, Inc. All rights reserved 2021
Networks of People Transaction Networks
Bought
B
ou
gh
t
V
i
e
w
e
d
R
e
t
u
r
n
e
d
Bought
Knowledge Networks
Pl
ay
s
Lives_in
In_sport
Likes
F
a
n
_
o
f
Plays_for
E.g., Risk management, Supply
chain, Payments
E.g., Employees, Customers,
Suppliers, Partners,
Influencers
E.g., Enterprise content,
Domain specific content,
eCommerce content
K
n
o
w
s
Knows
Knows
K
n
o
w
s
5
Connections in Data are as Valuable as the Data Itself
Neo4j, Inc. All rights reserved 2021
Native Graph Technology for Applications & Analytics
6
Analytics
Tooling
Graph Transactions
Data Integration
Dev.
& Admin
Drivers & APIs Discovery & Visualization
Graph Analytics
Developers
Admins
Applications Business Users
Data Analysts
Data Scientists
Enterprise Data Hub
Neo4j, Inc. All rights reserved 2021
Neo4j, Inc. All rights reserved 2021
7
Daten verfügbar!
Noch nicht für #btw21 aber für LTW Sachsen Anhalt
Neo4j, Inc. All rights reserved 2021
Download im CSV Format
bpb.de/politik/wahlen/wahl-o-mat/332469/download
ZIP mit CSV Dateien
Für einfachen Import auf GitHub
dev.neo4j.com/ltw-sa21
Import in sandbox.neo4j.com
Neo4j, Inc. All rights reserved 2021
CSV Format
• Partei
◦ Nr
◦ Name
◦ Kurzbezeichnung
• These
◦ Nr
◦ Titel
◦ Text
• Position
◦ Position - stimme zu, neutral, stimme nicht zu
◦ Begründung
Neo4j, Inc. All rights reserved 2021
Import
• Knoten
◦ Partei (21) (id, name, text)
◦ These (38) (id, name, text)
• Beziehung
◦ POSITION (798) (text, weight: 0.0-Ablehnung, 0.5-Neutral, 1.0-Zustimmung)
• via LOAD CSV + MERGE + CASE (siehe Script)
• Index on Parteiname/Thesenname
Neo4j, Inc. All rights reserved 2021
Neo4j, Inc. All rights reserved 2021
11
Exploration mit
Neo4j Browser + Bloom
Abfragen + Visuelles Clustering
Neo4j, Inc. All rights reserved 2021
Exploration in Neo4j Browser + Bloom
Neo4j, Inc. All rights reserved 2021
Ähnlichkeit von Parteien - "Abstand"
match (p1:Partei)-[r1:POSITION]->(t:These)<-[r2:POSITION]-(p2:Partei)
where id(p1)>id(p2)
return p1.name,p2.name, sum(abs(r1.weight-r2.weight)) as sim
order by sim asc;
Neo4j, Inc. All rights reserved 2021
Exploration in Neo4j Browser + Bloom
Neo4j, Inc. All rights reserved 2021
Neo4j, Inc. All rights reserved 2021
15
Graph Data Science
Abfragen + Visuelles Clustering
Neo4j, Inc. All rights reserved 2021
Neo4j for Graph Data Science™
16
Neo4j Graph Data
Science Library
Scalable Graph Algorithms
& Analytics Workspace
Native Graph
Creation & Persistence
Neo4j
Database
Visual Graph Exploration
& Prototyping
Neo4j
Bloom
Practical Integrated Intuitive
Neo4j, Inc. All rights reserved 2021
17
The Neo4j GDS Library
Robust Graph Algorithms (50+)
• Run on the loaded graph to compute metrics about the topology
and connectivity
• Highly parallelized and scale to 10’s of billions of nodes
Mutable In-Memory
Workspace
Computational Graph
Native Graph Store
Efficient & Flexible Analytics
Workspace
• Automatically reshapes transactional graphs
into an in-memory analytics graph
• Optimized for analytics with global traversals
and aggregation
• Create workflows and layer algorithms
Neo4j, Inc. All rights reserved 2021
Projected Graphs
• (Partei) - [:SIMILAR {weight}] - (Partei)
◦ virtuell in in-memory graph
◦ physisch als relationship
◦ "Distanz" als Gewicht normalisiert auf 0..1
• Clustering -> Louvain
• ML Model -> Link Prediction zur Thesis
Neo4j, Inc. All rights reserved 2021
Projection - virtuell
call gds.graph.create.cypher("parteien",
"MATCH (p:Partei) RETURN id(p) as id",
"MATCH
(p1:Partei)-[r1:POSITION]->(t:These)<-[r2:POSITION]-(p2:Partei)
RETURN id(p1) as source,id(p2) as target,
(29.0-sum(abs(r1.weight-r2.weight)))/29.0 as weight");
Neo4j, Inc. All rights reserved 2021
Louvain
• Louvain Algorithmus
• Clustering mit Gewicht
• 2 cluster
• Visualisierung mit Bloom
call gds.louvain.write("parteien",
{relationshipWeightProperty:'weight', writeProperty:'cluster'})
Neo4j, Inc. All rights reserved 2021
Projection - phyische Beziehung
MATCH
(p1:Partei)-[r1:POSITION]->(t:These)<-[r2:POSITION]-(p2:Partei)
WHERE id(p1)>id(p2)
WITH p1,p2, (29.0-sum(abs(r1.weight-r2.weight)))/29.0 AS weight
MERGE (p1)-[s:SIMILAR]-(p2)
SET s.weight = weight;
Neo4j, Inc. All rights reserved 2021
Node Embeddings & k-NN Similarity
• Node Embeddings -> Vektor repräsentiert "topologische Eigenschaften"
• kNN -> Ähnlichkeitsberechnung
1. load graph (undirected)
2. compute embedding (fastRP) + mutate in-memory graph
3. use embedding to compute k-NN topK (3) similarity
call gds.graph.create("p4",{Partei:{properties:"cluster"},These:{}},
{POSITION:{orientation:"UNDIRECTED",properties:"weight"}});
call gds.fastRP.mutate("p4",{relationshipWeightProperty:"weight",
embeddingDimension:128,
mutateProperty:"embedding"});
call gds.beta.knn.stream("p4",{nodeLabels:['Partei'],topK:3,
nodeWeightProperty:'embedding'})
yield node1, node2, similarity where node1 > node2
return gds.util.asNode(node1).name as n1,
gds.util.asNode(node2).name as n2, similarity
order by similarity desc LIMIT 10;
Neo4j, Inc. All rights reserved 2021
Train ML Model & Predictions Class (Cluster)
1. lade graph (undirected)
2. compute embedding mit fastRP
3. train model with embedding + class (cluster)
4. predict cluster
Keine tollen Ergebnisse,
Embedding muss verbessert
werden.
Neo4j, Inc. All rights reserved 2021
Weitere Ideen
• Gruppierung von Thesen
• eigene Position(en) hinzufügen und mit Parteien (und einander)
vergleichen (Wahlomat)
• Veränderung von Positionen der Parteien über Wahlperioden ->
Bewegung der Schwerpunkte
• Korrelation mit Wahlergebnissen
• Positionen pro Demographie / Bevölkerungssegment
• Link Prediction / Node Classification
• Mehrdimensionalität der Parteienlandschaft (nicht nur links-rechts)
Neo4j, Inc. All rights reserved 2021
Fragen & Diskussionen ? Gern am Stand nachfragen!
Neo4j, Inc. All rights reserved 2021
Selbst ausprobieren?
sandbox.neo4j.com dev.neo4j.com/gdsbk2
Neo4j, Inc. All rights reserved 2021
Neo4j, Inc. All rights reserved 2021
27
Danke für Ihre Aufmerksamkeit!
Alexander Erdl & Michael Hunger
Director Developer Relations Neo4j
dev.neo4j.com/gdsl
sandbox.neo4j.com
dev.neo4j.com/ltw-sa21
Neo4j, Inc. All rights reserved 2021
Train ML Model & Predict Link
1. load graph (with PRO/CON links?)
2. split relationships into test / train & positive (exists) & negative (doesn't
exist)
3. train model with existing links (works only on undirected graphs)
4. predict links
see GIST

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Einblicke ins Dickicht der Parteiprogramme

  • 1. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 1 Einblicke ins Dickicht der Parteiprogramme Data Science im Unternehmen Michael Hunger Director Developer Relations Neo4j dev.neo4j.com/ltw-sa21
  • 2. Neo4j, Inc. All rights reserved 2021 Es ist Wahljahr #btw2021 - Und wir alle gehen hin!
  • 3. Neo4j, Inc. All rights reserved 2021 Wahl-O-Mat® - Parteiprogramme im Vergleich
  • 4. Neo4j, Inc. All rights reserved 2021 Ziel: Visuelle und Algorithmische Analyse
  • 5. Neo4j, Inc. All rights reserved 2021 Networks of People Transaction Networks Bought B ou gh t V i e w e d R e t u r n e d Bought Knowledge Networks Pl ay s Lives_in In_sport Likes F a n _ o f Plays_for E.g., Risk management, Supply chain, Payments E.g., Employees, Customers, Suppliers, Partners, Influencers E.g., Enterprise content, Domain specific content, eCommerce content K n o w s Knows Knows K n o w s 5 Connections in Data are as Valuable as the Data Itself
  • 6. Neo4j, Inc. All rights reserved 2021 Native Graph Technology for Applications & Analytics 6 Analytics Tooling Graph Transactions Data Integration Dev. & Admin Drivers & APIs Discovery & Visualization Graph Analytics Developers Admins Applications Business Users Data Analysts Data Scientists Enterprise Data Hub
  • 7. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 7 Daten verfügbar! Noch nicht für #btw21 aber für LTW Sachsen Anhalt
  • 8. Neo4j, Inc. All rights reserved 2021 Download im CSV Format bpb.de/politik/wahlen/wahl-o-mat/332469/download ZIP mit CSV Dateien Für einfachen Import auf GitHub dev.neo4j.com/ltw-sa21 Import in sandbox.neo4j.com
  • 9. Neo4j, Inc. All rights reserved 2021 CSV Format • Partei ◦ Nr ◦ Name ◦ Kurzbezeichnung • These ◦ Nr ◦ Titel ◦ Text • Position ◦ Position - stimme zu, neutral, stimme nicht zu ◦ Begründung
  • 10. Neo4j, Inc. All rights reserved 2021 Import • Knoten ◦ Partei (21) (id, name, text) ◦ These (38) (id, name, text) • Beziehung ◦ POSITION (798) (text, weight: 0.0-Ablehnung, 0.5-Neutral, 1.0-Zustimmung) • via LOAD CSV + MERGE + CASE (siehe Script) • Index on Parteiname/Thesenname
  • 11. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 11 Exploration mit Neo4j Browser + Bloom Abfragen + Visuelles Clustering
  • 12. Neo4j, Inc. All rights reserved 2021 Exploration in Neo4j Browser + Bloom
  • 13. Neo4j, Inc. All rights reserved 2021 Ähnlichkeit von Parteien - "Abstand" match (p1:Partei)-[r1:POSITION]->(t:These)<-[r2:POSITION]-(p2:Partei) where id(p1)>id(p2) return p1.name,p2.name, sum(abs(r1.weight-r2.weight)) as sim order by sim asc;
  • 14. Neo4j, Inc. All rights reserved 2021 Exploration in Neo4j Browser + Bloom
  • 15. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 15 Graph Data Science Abfragen + Visuelles Clustering
  • 16. Neo4j, Inc. All rights reserved 2021 Neo4j for Graph Data Science™ 16 Neo4j Graph Data Science Library Scalable Graph Algorithms & Analytics Workspace Native Graph Creation & Persistence Neo4j Database Visual Graph Exploration & Prototyping Neo4j Bloom Practical Integrated Intuitive
  • 17. Neo4j, Inc. All rights reserved 2021 17 The Neo4j GDS Library Robust Graph Algorithms (50+) • Run on the loaded graph to compute metrics about the topology and connectivity • Highly parallelized and scale to 10’s of billions of nodes Mutable In-Memory Workspace Computational Graph Native Graph Store Efficient & Flexible Analytics Workspace • Automatically reshapes transactional graphs into an in-memory analytics graph • Optimized for analytics with global traversals and aggregation • Create workflows and layer algorithms
  • 18. Neo4j, Inc. All rights reserved 2021 Projected Graphs • (Partei) - [:SIMILAR {weight}] - (Partei) ◦ virtuell in in-memory graph ◦ physisch als relationship ◦ "Distanz" als Gewicht normalisiert auf 0..1 • Clustering -> Louvain • ML Model -> Link Prediction zur Thesis
  • 19. Neo4j, Inc. All rights reserved 2021 Projection - virtuell call gds.graph.create.cypher("parteien", "MATCH (p:Partei) RETURN id(p) as id", "MATCH (p1:Partei)-[r1:POSITION]->(t:These)<-[r2:POSITION]-(p2:Partei) RETURN id(p1) as source,id(p2) as target, (29.0-sum(abs(r1.weight-r2.weight)))/29.0 as weight");
  • 20. Neo4j, Inc. All rights reserved 2021 Louvain • Louvain Algorithmus • Clustering mit Gewicht • 2 cluster • Visualisierung mit Bloom call gds.louvain.write("parteien", {relationshipWeightProperty:'weight', writeProperty:'cluster'})
  • 21. Neo4j, Inc. All rights reserved 2021 Projection - phyische Beziehung MATCH (p1:Partei)-[r1:POSITION]->(t:These)<-[r2:POSITION]-(p2:Partei) WHERE id(p1)>id(p2) WITH p1,p2, (29.0-sum(abs(r1.weight-r2.weight)))/29.0 AS weight MERGE (p1)-[s:SIMILAR]-(p2) SET s.weight = weight;
  • 22. Neo4j, Inc. All rights reserved 2021 Node Embeddings & k-NN Similarity • Node Embeddings -> Vektor repräsentiert "topologische Eigenschaften" • kNN -> Ähnlichkeitsberechnung 1. load graph (undirected) 2. compute embedding (fastRP) + mutate in-memory graph 3. use embedding to compute k-NN topK (3) similarity call gds.graph.create("p4",{Partei:{properties:"cluster"},These:{}}, {POSITION:{orientation:"UNDIRECTED",properties:"weight"}}); call gds.fastRP.mutate("p4",{relationshipWeightProperty:"weight", embeddingDimension:128, mutateProperty:"embedding"}); call gds.beta.knn.stream("p4",{nodeLabels:['Partei'],topK:3, nodeWeightProperty:'embedding'}) yield node1, node2, similarity where node1 > node2 return gds.util.asNode(node1).name as n1, gds.util.asNode(node2).name as n2, similarity order by similarity desc LIMIT 10;
  • 23. Neo4j, Inc. All rights reserved 2021 Train ML Model & Predictions Class (Cluster) 1. lade graph (undirected) 2. compute embedding mit fastRP 3. train model with embedding + class (cluster) 4. predict cluster Keine tollen Ergebnisse, Embedding muss verbessert werden.
  • 24. Neo4j, Inc. All rights reserved 2021 Weitere Ideen • Gruppierung von Thesen • eigene Position(en) hinzufügen und mit Parteien (und einander) vergleichen (Wahlomat) • Veränderung von Positionen der Parteien über Wahlperioden -> Bewegung der Schwerpunkte • Korrelation mit Wahlergebnissen • Positionen pro Demographie / Bevölkerungssegment • Link Prediction / Node Classification • Mehrdimensionalität der Parteienlandschaft (nicht nur links-rechts)
  • 25. Neo4j, Inc. All rights reserved 2021 Fragen & Diskussionen ? Gern am Stand nachfragen!
  • 26. Neo4j, Inc. All rights reserved 2021 Selbst ausprobieren? sandbox.neo4j.com dev.neo4j.com/gdsbk2
  • 27. Neo4j, Inc. All rights reserved 2021 Neo4j, Inc. All rights reserved 2021 27 Danke für Ihre Aufmerksamkeit! Alexander Erdl & Michael Hunger Director Developer Relations Neo4j dev.neo4j.com/gdsl sandbox.neo4j.com dev.neo4j.com/ltw-sa21
  • 28. Neo4j, Inc. All rights reserved 2021 Train ML Model & Predict Link 1. load graph (with PRO/CON links?) 2. split relationships into test / train & positive (exists) & negative (doesn't exist) 3. train model with existing links (works only on undirected graphs) 4. predict links see GIST