# Calculating the Clustering Coefficient of a friend network

In this example, adapted from Niko Gamulins blog post on Neo4j for Social Network Analysis, the graph in question is showing the 2-hop relationships of a sample person as nodes with `KNOWS`

relationships.
The clustering coefficient of a selected node is defined as the probability that two randomly selected neighbors are connected to each other.
With the number of neighbors as `n`

and the number of mutual connections between the neighbors `r`

the calculation is
the number of possible connections between two neighbors is `n!/(2!(n-2)!) = 4!/(2!(4-2)!) = 24/4 = 6`

, where `n`

is the number of neighbors `n = 4`

and the actual number `r`

of connections is `1`

.
Therefore, the clustering coefficient of node 1 is `1/6`

.
`n`

and `r`

are quite simple to retrieve via the following query:

```
MATCH (a {name: "startnode"})--(b)
WITH a, count(distinct b) AS n
MATCH (a)--()-[r]-()--(a)
RETURN n, count(distinct r) AS r
```

This returns `n`

and `r`

for the above calculations.

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