Graph databases and analysis using Neo4j

This is an introductory level presentation on the topic of graph databases. It covers simple use-cases and basics.

1. Graph databases and analysis presents using Neo4j Akash Tandon Data Engineering@SocialCops Email: akash@socialcops.com Twitter: analyticalmonk
2. Goal - Learn why graph databases are relevant - Understand advantages and challenges related to working with Graph DBs - Introduction to Neo4j graph database, cypher query language and Py2Neo python package
3. History The Konigsberg problem Solved by Leonhard Euler Laid the foundation of graph theory Ref: Wikipedia
4. Why and what? Ref: Cambridge semantic blog
5. Use-cases - Fraud detection - Knowledge graphs - Recommendation systems - Investigative journalism (Panama papers) - Social media and network graphs - Analytics - … and so on!
6. Use-case: Panama papers https://neo4j.com/blog/analyzing-panama-papers-neo4j/
7. Neo4j - Most popular graph DBMS and market leader - Property graph database - Graph storage and processing engine - Open source and great community-support - Visualization tool, browser and integration with multiple languages (Python, Java, etc.)
8. Cypher - Declarative graph query language - Allows for expressive and efficient querying and updating of a property graph - SQL-ish
9. Cypher MATCH (a:Artist),(b:Album) WHERE a.Name = "Pink Floyd" AND b.Name = "Dark side of the moon" CREATE (a)-[r:RELEASED]->(b) RETURN r
10. Py2Neo - Py2neo is a client library and toolkit for working with Neo4j from within Python applications and from the command line. - Github repo: https://github.com/technige/py2neo
11. Py2Neo >>> from py2neo.data import Node, Relationship >>> a = Node("Person", name="Alice") >>> b = Node("Person", name="Bob") >>> ab = Relationship(a, "KNOWS", b) >>> ab (Alice)-[:KNOWS]->(Bob) -
12. Graph algorithms - Centralities (Pagerank, Betweenness, Closeness) - Community detection (Louvain, Label propogation) - Path finding (Shortest path - A*, Dijkstra) - Similarity (Jaccard, Cosine) Neo4j-supported graph algorithms: https://neo4j.com/docs/graph-algorithms/current/introducti on/#introduction-algorithms
13. Challenges - Scalability - Integration of data silos
14. Resources - https://neo4j.com/ - https://neo4j.com/docs/operations-manual/current/ installation/ - https://github.com/jbmusso/awesome-graph - https://tinkerpop.apache.org/ - https://www.analyticsvidhya.com/blog/2018/04/introdu ction-to-graph-theory-network-analysis-python-codes
15. Questions?
16. Thank you!