While SQL is a great tool for storing information such as usernames and passwords, it is not so great at allowing you to find connections among your users for the purposes of enhancing your website’s social experience.
The quickstart Flask tutorial builds a microblog application using SQLite.
In my tutorial, I walk through an expanded, Neo4j-powered version of this microblog application that uses py2neo, one of Neo4j’s Python drivers, to build social aspects into the application. This includes recommending similar users to the logged-in user, along with displaying similarities between two users when one user visits another user’s profile.
My microblog application consists of Users, Posts, and Tags modeled in Neo4j:
With this graph model, it is easy to ask questions such as:
“What are the top tags of posts that I’ve liked?”
MATCH (me:User)-[:LIKED]->(post:Post)<-[:TAGGED]-(tag:Tag) WHERE me.username = 'nicole' RETURN tag.name, COUNT(*) AS count ORDER BY count DESC
“Which user is most similar to me based on tags we’ve both posted about?”
MATCH (me:User)-[:PUBLISHED]->(:Post)<-[:TAGGED]-(tag:Tag), (other:User)-[:PUBLISHED]->(:Post)<-[:TAGGED]-(tag) WHERE me.username = 'nicole' AND me <> other WITH other,
- COLLECT(DISTINCT tag.name) AS tags,
- COUNT(DISTINCT tag) AS len
Links to the full walkthrough of the application and the complete code are below.
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About the Author
Nicole White, Data Scientist
Nicole White grew up in Kansas City, Missouri and then spent four years at LSU in Baton Rouge, Louisiana where she got a degree in economics with a minor in mathematics. She then went to the University of Texas at Austin where she got her masters degree in analytics, and it was during this time that she found Neo4j and began playing around with it. When she’s not graphing all the things, she spends her time playing card games and board games.