Discovering Hidden Connections with Neo4j: My First Real Use Case

#productivity #database

When I first heard about graph databases, I was curious but also skeptical. I had been working mostly with relational databases, and I thought, “Why would I need a new type of database when SQL already works fine?”

That mindset changed when I ran into a real-world problem. I needed to analyze relationships inside a university club where I’m an active member. We had multiple events, committees, and collaborations, and all the data was stored in spreadsheets. While it was easy to look up details about one member, it was extremely difficult to see connections between members. Who worked together the most? Who acted as a bridge between different committees? The answers weren’t obvious in rows and columns.

That’s when I decided to try Neo4j.

Why Neo4j?

Relational databases are great for structured data, but they struggle when you need to traverse multiple relationships quickly. For my use case, relationships mattered more than individual records. Neo4j’s model of nodes (entities) and relationships (connections) was a perfect fit.

This graph structure allowed me to ask questions that would be nearly impossible (or painfully slow) in SQL.

Importing the Data

I cleaned up the spreadsheet and imported the data into Neo4j. The Neo4j Browser made it simple, and soon I could visualize the network as a graph. Seeing members and their links for the first time was exciting—it already told me more than the tables ever did.

Running Cypher Queries

Here’s where the magic happened. Using Cypher, Neo4j’s query language, I was able to uncover hidden patterns.

For example, to find pairs of members who attended the most events together, I used:

MATCH (m:Member)-[:ATTENDED]->(e:Event)<-[:ATTENDED]-(n:Member)
WHERE m <> n
RETURN m.name AS Member1, n.name AS Member2, COUNT(e) AS SharedEvents
ORDER BY SharedEvents DESC
LIMIT 10;

This instantly showed me which members collaborated the most. Something that would’ve taken multiple complex SQL joins was just one neat query here.

Another interesting query was using PageRank, one of Neo4j’s built-in graph algorithms:

CALL gds.pageRank.stream({
  nodeProjection: 'Member',
  relationshipProjection: {
    ATTENDED: {
      type: 'ATTENDED',
      orientation: 'UNDIRECTED'
    }
  }
})
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS Member, score
ORDER BY score DESC
LIMIT 5;

This helped me identify the “influencers” in the club—the members most central to the network.

Visual Insights

The most rewarding part was seeing the visualization. Members clustered into natural groups based on committees and shared events. Some acted as connectors between groups, making them key people in keeping the community together.

This visualization not only gave us insights but also helped in decision-making—for example, assigning event leads or suggesting collaborations between members who hadn’t worked together yet.

What I Learned

This project taught me a few important lessons:

Relationships matter. When the problem is about connections, graph databases outperform relational ones.

Cypher is intuitive. Once I learned the basics, writing queries felt almost natural.

Visualization adds value. Data is more persuasive when you can see it.

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