Neo4j is a graph database that uses a labeled property graph model to store and query data. It allows for complex relationship queries using a simple declarative query language called Cypher. Data can be accessed and manipulated through a REST API or native graph database drivers. Common graph algorithms like centrality measures, minimum spanning trees, and community detection are available through Neo4j's algorithm library. The document provides examples of social network, movie recommendation, and IP mapping applications built using Neo4j.

1. Introduction to neo4j
Chakrit Phain
Softnix Technology Co.,Ltd

2. Neo4j Platform

3. The Labeled Property Graph Model

4. Relational Databases

8. SQL Query Syntax
SELECT name FROM Person
LEFT JOIN Person_Department
ON Person.Id =
Person_Department.PersonId
LEFT JOIN Department
ON Department.Id =
Person_Department.DepartmentId
WHERE Department.name = "IT
Department"

9. Neo4j Query Syntax
MATCH (p:Person)<-
[:EMPLOYEE]-(d:Department)
WHERE d.name = "IT
Department" RETURN p.name

10. REST API
:POST
http://localhost:7474/db/data/transaction/commit
{"statements":[ {"statement":"CREATE (p:Person
{name:{name}}) RETURN p",
"parameters":{"name":"Daniel"}} ]}
->
{"results":[{"columns":["p"],"data":[{"row":[{"name":"D
aniel"}]}]}],"errors":[]}

11. Driver support
Connection con =
DriverManager.getConnection("jdbc:neo4j://localhost:7
474/");
String query = "MATCH (:Person {name:{1}})-
[:EMPLOYEE]-(d:Department) RETURN d.name as
dept";
try (PreparedStatement stmt =
con.prepareStatement(QUERY)) {
stmt.setString(1,"John");
ResultSet rs = stmt.executeQuery();

12. Create

13. Read-Write

14. Delete &
Remove

15. Let’s Play

16. Find path & Find shortest path
• match (n)-[*]->(m) where
m.name=‘Harry’ and
n.name=’Ron' return (n)-[*]-
>(m)
• match (n)-[*]->(m) where
m.name=Harry’ and
n.name=’Ron' return
shortestpath((n)-[*]->(m))

17. Find Degree
• match (n:Person)-[r]-(m:Person)
return n.name, count(r) as DegreeScore
order by DegreeScore desc
limit 10;

18. Betweenness
centrality

19. Closeness
Centrality

20. Minimum Weight Spanning Tree

21. Neo4j Graph Algorithms
• https://github.com/neo4j-contrib/neo4j-graph-algorithms/releases
• Centralities:
• Page Rank (algo.pageRank)
• Betweenness Centrality (algo.betweenness)
• Closeness Centrality (algo.closeness)
• Community Detection:
• Louvain (algo.louvain)
• Label Propagation (algo.labelPropagation)
• (Weakly) Connected Components (algo.unionFind)
• Strongly Connected Components (algo.scc)
• Triangle Count / Clustering Coefficient (algo.triangleCount)

22. Movies Finder

23. Email Finder

24. IP Network

25. Question ?