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A Modern Approach to
Connected Data
Practical Graph Algorithms with
Neo4j
About me
Head of Developer Relations Engineering
Follow & tweet at me @mesirii
(graphs)-[:ARE]->(everywhere)
Value from Data Relationships
Common Current Use Cases
Internal Applications
Master Data Management
Network and
IT Operations
Fraud Detection
Customer-Facing Applications
Real-Time Recommendations
Graph-Based Search
Identity and
Access Management
http://neo4j.com/use-cases
Insights from Algorithms
Improving all use-cases
Graph Algorithms
Relevance
Clustering
Structural Insights
Machine Learning
Classification, Regression
NLP, Struct/Content Pred
NN <-> Graph
Graph As Compute
http://neo4j.com/use-cases
Example: Twitter Analytics
Right Relevance
neo4j.com/blog/graph-algorithms-make-election-data-great-again
Reasoning RR
Custom Pipeline Neo4j <-> R
Goal: Simplification & Performance
● PageRank
● Clustering
● Weighted Similarities
● ... prop. IP ...
Applicable Tools
• Store Tweets in Neo4j (e.g. 300M)
• Use APOC + Graph Algorithms for processing
• Use NLP Algorithms / Procedures (e.g. from GraphAware)
• Neo4j Tableau (WDC) Connector
• Use APOC for streaming results to Gephi
Graph Algorithms
Project Goals
• high performance graph
algorithms
• user friendly (procedures)
• support graph projections
• augment OLTP with OLAP
• integrate efficiently with live
Neo4j database
(read, write, Cypher)
• common Graph API to write your
own
Kudos
Development
• Martin Knobloch (AVGL)
• Paul Horn (AVGL)
Documentation
• Tomaz Bratanic
Usage 1. Call as Cypher procedure
2. Pass in specification (Label, Prop, Query) and
configuration
3. ~.stream variant returns (a lot) of results
CALL algo.<name>.stream('Label','TYPE',{conf})
YIELD nodeId, score
4. non-stream variant writes results to graph
returns statistics
CALL algo.<name>('Label','TYPE',{conf});
5. Cypher projection:
pass in Cypher for node- and relationship-lists
CALL algo.<name>(
'MATCH ... RETURN id(n)',
'MATCH (n)-->(m) RETURN id(n), id(m)',
{graph:'cypher'})
Architecture
1. Load Data in parallel
from Neo4j
2. Store in efficient
Datastructure
3. Run Graph Algorithm
in parallel using
Graph API
4. Write data back in
parallel
Neo4j
1, 2
Algorithm
Datastructures
4
3
Graph API
TheAlgorithms
Centralities
•PageRank (baseline)
•Betweeness
•Closeness
•Degree
Example: PageRank
https://neo4j-contrib.github.io/neo4j-graph-algorithms/#_page_rank
CALL algo.pageRank.stream
('Page', 'LINKS', {iterations:20, dampingFactor:0.85})
YIELD node, score
RETURN node,score order by score desc limit 20
CALL algo.pageRank('Page', 'LINKS',
{iterations:20, dampingFactor:0.85,
write: true,writeProperty:"pagerank"})
YIELD nodes, iterations, loadMillis, computeMillis,
writeMillis, dampingFactor, write, writeProperty
Clustering
•Label Propagation
•Louvain (Phase2)
•Union Find / WCC
•Strongly Connected
Components
•Triangle-Count/Clusteri
ng CoeffTheAlgorithms
Example: UnionFind (CC)
https://neo4j-contrib.github.io/neo4j-graph-algorithms/#_connected_components
CALL algo.unionFind.stream('User', 'FRIEND', {})
YIELD nodeId,setId
RETURN setId, count(*)
ORDER BY count(*) DESC LIMIT 100;
CALL algo.unionFind('User', 'FRIEND',
{write:true, partitionProperty:'partition'})
YIELD nodes, setCount, loadMillis, computeMillis,
writeMillis
Path-Expansion / Traversal
• Single Short Path
• All-Nodes SSP
• Parallel BFS / DFS
TheAlgorithms
MATCH(start:Loc {name:'A'})
CALL algo.deltaStepping.stream(start, 'cost', 3.0)
YIELD nodeId, distance
RETURN nodeId, distance ORDER LIMIT 20
MATCH(start:Node {name:'A'})
CALL algo.deltaStepping(start, 'cost', 3.0,
{write:true, writeProperty:'ssp'})
YIELD nodeCount, loadDuration, evalDuration,
writeDuration
RETURN *
Example: UnionFind (CC)
https://neo4j-contrib.github.io/neo4j-graph-algorithms/#_all_pairs_and_single_source_shortest_path
DEMO
Datasets
● Neo4j Community Graph
○ 280k nodes, 1.4m relationships
○ Centralities, Clustering, Grouping
● DBPedia
○ 11m nodes, 116m relationships
○ Page Rank, unionFind
● Bitcoin
○ 1.7bn nodes, 2.7bn rels
○ degree distribution
○ pageRank, unionFind
Neo4j Community Graph
● Neo4j Community activity from Twitter, GitHub, StackOverflow
● Let's look at tweets
● Tweet-PageRank
● Projection -> mention-user-network
○ centralities
○ clustering
○ grouping
DBPedia
● Shallow Copy of Wikipedia
● Just (Page) -[:Link]-> (Page)
CALL algo.pageRank.stream('Page', 'Link', {iterations:5}) YIELD node, score
WITH * ORDER BY score DESC LIMIT 5
RETURN node.title, score;
+--------------------------------------+
| node.title | score |
+--------------------------------------+
| "United States" | 13349.2 |
| "Animal" | 6077.77 |
| "France" | 5025.61 |
| "List of sovereign states" | 4913.92 |
| "Germany" | 4662.32 |
+--------------------------------------+
5 rows
46247 ms
DBPedia
CALL algo.pageRank('Page', 'Link', {write:true,iterations:20});
+--------------------------------------------------------------------------------------+
| nodes | iter | loadMillis | computeMillis | writeMillis | damping | writeProperty |
+--------------------------------------------------------------------------------------+
| 11474730 | 20 | 34106 | 9712 | 1810 | 0.85 | "pagerank" |
+--------------------------------------------------------------------------------------+
1 row
47888 ms
BitCoin
● Full Copy of the BitCoin BlockChain
○ from learnmeabitcoin.com
○ thanks Greg Walker
○ (see Online Meetup)
● 1.7bn nodes, 2.7bn rels
○ 474k blocks,
○ 240m tx,
○ 280m addresses
○ 650m outputs
BitCoin
● distribution of "locked" relationships for "addresses"
○ = participation in transactions
call apoc.stats.degrees('<locked');
+--------------------------------------------------------------------------------------------------------------+
| type | direction | total | p50 | p75 | p90 | p95 | p99 | p999 | max | min | mean |
+--------------------------------------------------------------------------------------------------------------+
| "locked" | "INCOMING" | 654662356 | 0 | 0 | 1 | 1 | 2 | 28 | 1891327 | 0 | 0.37588608290716047 |
+--------------------------------------------------------------------------------------------------------------+
1 row
308619 ms
● Inferred network of addresses, via transaction and output
● (a)<-[:locked]-(o)-[:in]->(tx)-[:out]->(o2)-[:locked]->(a2)
● use cypher projections
● 1M outputs (24s) to start with, connected addresses via tx
● 10M outputs (296s)
call algo.unionFind.stream(
'match (o:output)-[:locked]->(a) with a limit 10000000
return id(a) as id',
'match (o:output)-[:locked]->(a) with o,a limit 10000000
match (o)-[:in]->(tx)-[:out]->(o2)-[:locked]->(a2)
return id(a) as source, id(a2) as target, count(tx) as value',
{graph:'cypher'}) YIELD setId, nodeId
RETURn setId, count(*) as size
ORDER BY size DESC LIMIT 10;
BitCoin
+-------------------+
| setId | size |
+-------------------+
| 5036 | 4409420 |
| 6295282 | 1999 |
| 5839746 | 1488 |
| 9356302 | 833 |
| 6560901 | 733 |
| 6370777 | 637 |
| 8101710 | 392 |
| 5945867 | 369 |
| 2489036 | 264 |
| 1703620 | 203 |
+-------------------+
10 rows
296109 ms
First release July 2017
• neo4j.com/blog/efficient-graph-algorithms-neo4j
Second Release Sept/Oct 2017
• huge graphs, additional algorithms, bugfixes
Timing
• Launch & Perf:
• Docs: neo4j-contrib.github.io/neo4j-graph-algorithms
• Tomaz Bratanic: tbgraph.wordpress.com (docs, social, tube, GoT)
• Community Graph: github.com/community-graph
• Twitter Analytics:
neo4j.com/blog/graph-algorithms-make-election-data-great-again
More examples
Reading Material
• Thanks to Amy Hodler
• 13 Top Resources on Graph Theory
• neo4j.com/blog/
top-13-resources-graph-theory-algorithms/
We need your feedback & use-cases!
neo4j.com/slack -> #neo4j-graph-algorithms
github.com/neo4j-contrib/neo4j-graph-algorithms
michael@neo4j.com
Please get in touch!!
The Sky is the Limit
Questions?
Don't forget! Subscribe & Like
youtube.com/c/neo4j

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Practical Graph Algorithms with Neo4j

  • 1. A Modern Approach to Connected Data Practical Graph Algorithms with Neo4j
  • 2. About me Head of Developer Relations Engineering Follow & tweet at me @mesirii
  • 4. Value from Data Relationships Common Current Use Cases Internal Applications Master Data Management Network and IT Operations Fraud Detection Customer-Facing Applications Real-Time Recommendations Graph-Based Search Identity and Access Management http://neo4j.com/use-cases
  • 5. Insights from Algorithms Improving all use-cases Graph Algorithms Relevance Clustering Structural Insights Machine Learning Classification, Regression NLP, Struct/Content Pred NN <-> Graph Graph As Compute http://neo4j.com/use-cases
  • 6. Example: Twitter Analytics Right Relevance neo4j.com/blog/graph-algorithms-make-election-data-great-again
  • 9. Goal: Simplification & Performance ● PageRank ● Clustering ● Weighted Similarities ● ... prop. IP ...
  • 10. Applicable Tools • Store Tweets in Neo4j (e.g. 300M) • Use APOC + Graph Algorithms for processing • Use NLP Algorithms / Procedures (e.g. from GraphAware) • Neo4j Tableau (WDC) Connector • Use APOC for streaming results to Gephi
  • 12. Project Goals • high performance graph algorithms • user friendly (procedures) • support graph projections • augment OLTP with OLAP • integrate efficiently with live Neo4j database (read, write, Cypher) • common Graph API to write your own
  • 13. Kudos Development • Martin Knobloch (AVGL) • Paul Horn (AVGL) Documentation • Tomaz Bratanic
  • 14. Usage 1. Call as Cypher procedure 2. Pass in specification (Label, Prop, Query) and configuration 3. ~.stream variant returns (a lot) of results CALL algo.<name>.stream('Label','TYPE',{conf}) YIELD nodeId, score 4. non-stream variant writes results to graph returns statistics CALL algo.<name>('Label','TYPE',{conf}); 5. Cypher projection: pass in Cypher for node- and relationship-lists CALL algo.<name>( 'MATCH ... RETURN id(n)', 'MATCH (n)-->(m) RETURN id(n), id(m)', {graph:'cypher'})
  • 15. Architecture 1. Load Data in parallel from Neo4j 2. Store in efficient Datastructure 3. Run Graph Algorithm in parallel using Graph API 4. Write data back in parallel Neo4j 1, 2 Algorithm Datastructures 4 3 Graph API
  • 17. Example: PageRank https://neo4j-contrib.github.io/neo4j-graph-algorithms/#_page_rank CALL algo.pageRank.stream ('Page', 'LINKS', {iterations:20, dampingFactor:0.85}) YIELD node, score RETURN node,score order by score desc limit 20 CALL algo.pageRank('Page', 'LINKS', {iterations:20, dampingFactor:0.85, write: true,writeProperty:"pagerank"}) YIELD nodes, iterations, loadMillis, computeMillis, writeMillis, dampingFactor, write, writeProperty
  • 18. Clustering •Label Propagation •Louvain (Phase2) •Union Find / WCC •Strongly Connected Components •Triangle-Count/Clusteri ng CoeffTheAlgorithms
  • 19. Example: UnionFind (CC) https://neo4j-contrib.github.io/neo4j-graph-algorithms/#_connected_components CALL algo.unionFind.stream('User', 'FRIEND', {}) YIELD nodeId,setId RETURN setId, count(*) ORDER BY count(*) DESC LIMIT 100; CALL algo.unionFind('User', 'FRIEND', {write:true, partitionProperty:'partition'}) YIELD nodes, setCount, loadMillis, computeMillis, writeMillis
  • 20. Path-Expansion / Traversal • Single Short Path • All-Nodes SSP • Parallel BFS / DFS TheAlgorithms
  • 21. MATCH(start:Loc {name:'A'}) CALL algo.deltaStepping.stream(start, 'cost', 3.0) YIELD nodeId, distance RETURN nodeId, distance ORDER LIMIT 20 MATCH(start:Node {name:'A'}) CALL algo.deltaStepping(start, 'cost', 3.0, {write:true, writeProperty:'ssp'}) YIELD nodeCount, loadDuration, evalDuration, writeDuration RETURN * Example: UnionFind (CC) https://neo4j-contrib.github.io/neo4j-graph-algorithms/#_all_pairs_and_single_source_shortest_path
  • 22. DEMO
  • 23. Datasets ● Neo4j Community Graph ○ 280k nodes, 1.4m relationships ○ Centralities, Clustering, Grouping ● DBPedia ○ 11m nodes, 116m relationships ○ Page Rank, unionFind ● Bitcoin ○ 1.7bn nodes, 2.7bn rels ○ degree distribution ○ pageRank, unionFind
  • 24. Neo4j Community Graph ● Neo4j Community activity from Twitter, GitHub, StackOverflow ● Let's look at tweets ● Tweet-PageRank ● Projection -> mention-user-network ○ centralities ○ clustering ○ grouping
  • 25. DBPedia ● Shallow Copy of Wikipedia ● Just (Page) -[:Link]-> (Page) CALL algo.pageRank.stream('Page', 'Link', {iterations:5}) YIELD node, score WITH * ORDER BY score DESC LIMIT 5 RETURN node.title, score; +--------------------------------------+ | node.title | score | +--------------------------------------+ | "United States" | 13349.2 | | "Animal" | 6077.77 | | "France" | 5025.61 | | "List of sovereign states" | 4913.92 | | "Germany" | 4662.32 | +--------------------------------------+ 5 rows 46247 ms
  • 26. DBPedia CALL algo.pageRank('Page', 'Link', {write:true,iterations:20}); +--------------------------------------------------------------------------------------+ | nodes | iter | loadMillis | computeMillis | writeMillis | damping | writeProperty | +--------------------------------------------------------------------------------------+ | 11474730 | 20 | 34106 | 9712 | 1810 | 0.85 | "pagerank" | +--------------------------------------------------------------------------------------+ 1 row 47888 ms
  • 27. BitCoin ● Full Copy of the BitCoin BlockChain ○ from learnmeabitcoin.com ○ thanks Greg Walker ○ (see Online Meetup) ● 1.7bn nodes, 2.7bn rels ○ 474k blocks, ○ 240m tx, ○ 280m addresses ○ 650m outputs
  • 28. BitCoin ● distribution of "locked" relationships for "addresses" ○ = participation in transactions call apoc.stats.degrees('<locked'); +--------------------------------------------------------------------------------------------------------------+ | type | direction | total | p50 | p75 | p90 | p95 | p99 | p999 | max | min | mean | +--------------------------------------------------------------------------------------------------------------+ | "locked" | "INCOMING" | 654662356 | 0 | 0 | 1 | 1 | 2 | 28 | 1891327 | 0 | 0.37588608290716047 | +--------------------------------------------------------------------------------------------------------------+ 1 row 308619 ms
  • 29. ● Inferred network of addresses, via transaction and output ● (a)<-[:locked]-(o)-[:in]->(tx)-[:out]->(o2)-[:locked]->(a2) ● use cypher projections ● 1M outputs (24s) to start with, connected addresses via tx ● 10M outputs (296s) call algo.unionFind.stream( 'match (o:output)-[:locked]->(a) with a limit 10000000 return id(a) as id', 'match (o:output)-[:locked]->(a) with o,a limit 10000000 match (o)-[:in]->(tx)-[:out]->(o2)-[:locked]->(a2) return id(a) as source, id(a2) as target, count(tx) as value', {graph:'cypher'}) YIELD setId, nodeId RETURn setId, count(*) as size ORDER BY size DESC LIMIT 10; BitCoin +-------------------+ | setId | size | +-------------------+ | 5036 | 4409420 | | 6295282 | 1999 | | 5839746 | 1488 | | 9356302 | 833 | | 6560901 | 733 | | 6370777 | 637 | | 8101710 | 392 | | 5945867 | 369 | | 2489036 | 264 | | 1703620 | 203 | +-------------------+ 10 rows 296109 ms
  • 30. First release July 2017 • neo4j.com/blog/efficient-graph-algorithms-neo4j Second Release Sept/Oct 2017 • huge graphs, additional algorithms, bugfixes Timing
  • 31. • Launch & Perf: • Docs: neo4j-contrib.github.io/neo4j-graph-algorithms • Tomaz Bratanic: tbgraph.wordpress.com (docs, social, tube, GoT) • Community Graph: github.com/community-graph • Twitter Analytics: neo4j.com/blog/graph-algorithms-make-election-data-great-again More examples
  • 32. Reading Material • Thanks to Amy Hodler • 13 Top Resources on Graph Theory • neo4j.com/blog/ top-13-resources-graph-theory-algorithms/
  • 33. We need your feedback & use-cases! neo4j.com/slack -> #neo4j-graph-algorithms github.com/neo4j-contrib/neo4j-graph-algorithms michael@neo4j.com Please get in touch!!
  • 34. The Sky is the Limit Questions?
  • 35. Don't forget! Subscribe & Like youtube.com/c/neo4j