# Common Neighbors

Common neighbors captures the idea that two strangers who have a friend in common are more likely to be introduced than those who don’t have any friends in common.

This feature is in the alpha tier. For more information on feature tiers, see API Tiers.

## 1. History and explanation

It is computed using the following formula: where `N(x)` is the set of nodes adjacent to node `x`, and `N(y)` is the set of nodes adjacent to node `y`.

A value of 0 indicates that two nodes are not close, while higher values indicate nodes are closer.

The library contains a function to calculate closeness between two nodes.

## 2. Syntax

The following will run the algorithm and return the result:
``````RETURN gds.alpha.linkprediction.commonNeighbors(node1:Node, node2:Node, {
relationshipQuery:String,
direction:String
})``````
Table 1. Parameters
Name Type Default Optional Description

`node1`

Node

null

no

A node

`node2`

Node

null

no

Another node

`relationshipQuery`

String

null

yes

The relationship type used to compute similarity between `node1` and `node2`.

`direction`

String

BOTH

yes

The relationship direction used to compute similarity between `node1` and `node2`. Possible values are `OUTGOING`, `INCOMING` and `BOTH`.

## 3. Common Neighbors algorithm sample

The following will project a sample graph:
``````CREATE
(zhen:Person {name: 'Zhen'}),
(praveena:Person {name: 'Praveena'}),
(michael:Person {name: 'Michael'}),
(arya:Person {name: 'Arya'}),
(karin:Person {name: 'Karin'}),

(zhen)-[:FRIENDS]->(arya),
(zhen)-[:FRIENDS]->(praveena),
(praveena)-[:WORKS_WITH]->(karin),
(praveena)-[:FRIENDS]->(michael),
(michael)-[:WORKS_WITH]->(karin),
(arya)-[:FRIENDS]->(karin)``````
The following will return the number of common neighbors for Michael and Karin:
`````` MATCH (p1:Person {name: 'Michael'})
MATCH (p2:Person {name: 'Karin'})
Table 2. Results
`score`

1.0

We can also compute the score of a pair of nodes based on a specific relationship type.

The following will return the number of common neighbors for Michael and Karin based only on the `FRIENDS` relationships:
`````` MATCH (p1:Person {name: 'Michael'})
MATCH (p2:Person {name: 'Karin'})
RETURN gds.alpha.linkprediction.commonNeighbors(p1, p2, {relationshipQuery: "FRIENDS"}) AS score``````
Table 3. Results
`score`

0.0