Скачать или смотреть Discovering Mutual Relationships in Graph Databases with Gremlin

Learn how to query mutual relationships in graph databases using Gremlin, including practical examples and solutions for your graph data challenges.
---
This video is based on the question https://stackoverflow.com/q/64062795/ asked by the user 'Jack Price-Burns' ( https://stackoverflow.com/u/2710382/ ) and on the answer https://stackoverflow.com/a/64064055/ provided by the user 'Kelvin Lawrence' ( https://stackoverflow.com/u/5442034/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Gremlin, get two vertices that both have an edge to each other

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Discovering Mutual Relationships in Graph Databases with Gremlin

In the world of graph databases, understanding the relationships between entities is key. A common scenario arises when we want to find mutual relationships — pairs of vertices that are connected by an edge in both directions. For instance, consider a social network where one user "A" likes another user "B." This interaction results in a directed edge from A to B. However, if B also likes A, that's the mutual relationship we want to identify.

The Problem

Imagine having a graph that represents 2000 users, each capable of liking one another. The challenge is to find all user pairs where mutual likes exist, meaning that if A likes B, then B also likes A. Notably, simple queries using .both('likes') aren't enough, as they gather vertices that are either liked by a person or who like them, instead of focusing on mutual connections.

Example with B

For example:

A likes B (A - B)

B likes A (B - A)

The goal is to find pairs like (A, B) where both relationships exist.

The Solution

To effectively query mutual relationships in Gremlin, we can utilize specific graph traversal steps. Here’s how to approach the problem.

Step 1: Basic Query Structure

The essence of finding mutual relationships lies in the query syntax. Here's a Gremlin query that captures this:

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the Query:

g.V(): Start at all vertices.

hasLabel('user').as('a'): Filters to only include vertices labeled as users, labeling them as 'a'.

out('likes').as('b'): Traverses out from 'a' to find who they like, labeling those as 'b'.

where(__.in('likes').as('a')): Checks if there’s an incoming 'likes' edge back to 'a' from 'b', confirming a mutual relationship.

select('a','b'): This part of the query collects the matched user pairs.

Step 2: Handling Duplicate Relationships

The above query returns each user pair twice, once for each direction (A likes B and B likes A). To eliminate duplicates, apply a dedup step:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Finding Non-Mutual Relationships

To explore non-mutual relationships (where one user likes another but not vice versa), a not step can be added:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

In summary, querying mutual relationships in a graph database using Gremlin involves understanding the bidirectional nature of relationships. Through the methods outlined above, you can effectively identify pairs of users with mutual likes. Using dedup eliminates duplicate pairs, while the not step helps in finding one-sided relationships. This ability to dissect relationships is not only vital for user analysis in social networks but also applicable in various domains that rely on relationship dynamics.

Feel free to apply these techniques to your data, and explore the intricate web of connections!