Role of Graph Databases in ML and non-ML use-cases

Introduction

Laymen explanation

When you are using the social media, you will see real need of graph. For example, you will need to have followers analysis and then you will need users in graph who are followers to you. Same way, you will ask additional info like tweets done by this user, hashtag used by this user.

Similarly, for ML recommendation engine, you will need graph. for example, similar movies to watch can be represented by graph.

Graph DB helps to achieve all of above

Technical explanation

A graph database (GDB) is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data.[1]A key concept of the system is the graph (or edge or relationship).

Properties of graph databases

• Graph databases are part of the NoSQL databases.

  • Retrieving data from a graph database requires a query language other than SQL. There are multi-vendor query languages like Gremlin, SPARQL, and Cypher

• In addition to having query language interfaces, some graph databases are accessed through application programming interfaces (APIs).

  • Graph databases portray the data as it is viewed conceptually. A graph within graph databases is based on graph theory.

Benefits

• Graph databases are a powerful tool for graph-like queries. For example, computing the shortest path between two nodes in the graph

  • Graphs are flexible, meaning it allows the user to insert new data into the existing graph without loss of application functionality. There is no need for the designer of the database to plan out extensive details of the database's future use cases.

Example of graph databases

• Neo4j

• GraphDB

  • ArangoDB

  • ...

How to select which Graph DBMS is good?

There are many Graph databases in market and each have specific merit.

So, Instead of discussing which graph language is the best, or fusing the best aspects of each graph language into one new, unified option, it’s worth taking a step back to ask a more fundamental question: What are the prerequisites for a graph query language in the first place?

Pre-requisites

• Finer control on graph traversal.

• A graph model and graph query language should embrace data independence. To be more precise, a graph query language should support the definition of logical graph schemas.

• development language which you are using. See if the DBMS supports that language or not

  • Batch query support. For example, for a product recommendation problem, they want to find recommendations for each and every customer.

Other criteria

• Query execution speed

• Update and insert execution speed

• Scalability

• Ease-of-use

Real life use-case for graph DB

Twitter example

Below graph provides feeling of graph related to tweet. It gives feeling of tweets and relationship of them as an overview.

Below is the simpler textual representation of list of tweets

Refer here for more use-cases.

Reference

https://en.wikipedia.org/wiki/Graph_database

https://en.wikipedia.org/wiki/DB-Engines_ranking

https://en.wikipedia.org/wiki/Neo4j

https://neo4j.com/developer/get-started/

https://neo4j.com/graphgist/learning-cypher-with-san-francisco-bay-map

https://db-engines.com/en/system/GraphDB%3BNeo4j

https://www.eweek.com/database/what-enterprises-should-know-about-selecting-a-graph-query-language/

https://bitnine.net/blog-graph-database/graph-database-comparison-and-analysis-storageclustering/

https://images.app.goo.gl/p1y3UvjppDjmrJS88

https://neo4j.com/developer/cypher/guide-build-a-recommendation-engine/

https://www.linkedin.com/posts/dpkumar_graphdatabases-machinelearning-nosqldatabases-activity-6789441467191574528-PuGx