---
title: same_community
url: https://nexyron.tech/docs/cypher/functions/vector-and-similarity/same-community
description: Checks whether two community ids are equal. It is documented separately because similarity orientation, vector dimensions, and set semantics directly affect ranking and interpretat
---

# same_community

## Generic Description

Checks whether two community ids are equal. It is documented separately because similarity orientation, vector dimensions, and set semantics directly affect ranking and interpretation.

Simple example:

```cypher
RETURN same_community(7, 7) AS value
```

## Consumer-Level Explanation

Use it after community detection when later query stages need a readable same-group predicate. Prefer this function when symbolic graph filters and similarity evidence should remain in one auditable Cypher pipeline.

## More Detailed Explanation

`same_community` keeps similarity scoring in the same Cypher pipeline as symbolic graph filters and document metadata checks. That lets a query combine labels, relationships, time filters, and embedding or set overlap scores without asking a client to reconcile separate result sets.

## Advanced Example

This example applies `same_community` after symbolic graph filtering, which keeps hybrid retrieval constraints and similarity scoring in one visible pipeline.

```cypher
WITH [{left: 'acct-1', right: 'acct-2', left_community: 4, right_community: 4}, {left: 'acct-1', right: 'acct-9', left_community: 4, right_community: 8}] AS pairs
UNWIND pairs AS pair
RETURN pair.left AS left_entity, pair.right AS right_entity, same_community(pair.left_community, pair.right_community) AS same_group
ORDER BY left_entity, right_entity
```

## Real Use Cases

-   semantic ranking over embedded documents after graph/time filtering
-   link-prediction features based on shared neighbors or metadata overlap
-   hybrid retrieval where symbolic constraints and vector scores are both visible

## Real Limitations And Tradeoffs

-   vector functions require compatible dimensions and meaningful embedding spaces
-   set-similarity functions treat lists as sets and may ignore duplicate frequency
-   a high similarity score is evidence, not proof of domain equivalence