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# Local Clustering Coefficient

✓ File Writeback ✓ Property Writeback ✓ Direct Return ✓ Stream Return ✕ Stats

## Overview

The Local Clustering Coefficient algorithm calculates the density of connection among the immediate neighbors of a node. It quantifies the ratio of actual connections among the neighbors to the maximum possible connections.

The local clustering coefficient provides insights into the cohesion of a node's ego network. In the context of a social network, the local clustering coefficient helps understand the degree of interconnectedness among an individual's friends or acquaintances. A high local clustering coefficient suggests that the person's friends are likely to be connected to each other, indicating the presence of a closely-knit social group, such as a family. Conversely, a low local clustering coefficient indicates a more dispersed or loosely interconnected ego network, where the person's friends do not have strong connections with each other.

## Concepts

### Local Clustering Coefficient

Mathematically, the local clustering coefficient of a node in an undirected graph is calculated as the ratio of the number of connected neighbor pairs to the total number of possible neighbor pairs: where n is the number of nodes contained in the 1-hop neighborhood of node v (denoted as N(v)), i and j are any two distinct nodes within N(v), δ(i,j) is equal to 1 if i and j are connected, and 0 otherwise. In this example, the local clustering coefficient of the red node is 1/(5*4/2) = 0.1.

## Considerations

• The Local Clustering Coefficient algorithm ignores the direction of edges but calculates them as undirected edges.

## Syntax

• Command: `algo(clustering_coefficient)`
• Parameters:
Name
Type
Spec
Default
Optional
Description
ids / uuids []`_id` / []`_uuid` / / Yes ID/UUID of nodes to calculate the local clustering coefficient, calculate for all nodes if not set
limit int ≥-1 `-1` Yes Number of results to return, `-1` to return all results
order string `asc`, `desc` / Yes Sort nodes by the value of the local clustering coefficient

## Examples

The example graph is as follows: ### File Writeback

Spec Content
filename `_id`,`centrality`
``````algo(clustering_coefficient).params({
ids: ['Lee', 'Choi']
}).write({
file:{
filename: 'lcc'
}
})
``````

Results: File lcc

``````Lee,0.266667
Choi,1
``````

### Property Writeback

Spec Content Write to Data Type
property `centrality` Node property `float`
``````algo(clustering_coefficient).params().write({
db:{
property: 'lcc'
}
})
``````

Results: The value of the local clustering coefficient for each node is written to a new property named lcc

### Direct Return

Alias Ordinal
Type
Description
Columns
0 []perNode Node and its local clustering coefficient `_uuid`, `centrality`
``````algo(clustering_coefficient).params({
order: 'desc'
}) as lcc
return lcc
``````

Results: lcc

_uuid centrality
5 1
2 1
7 0.666667
4 0.666667
3 0.666667
1 0.266667
6 0

### Stream Return

Alias Ordinal
Type
Description
Columns
0 []perNode Node and its local clustering coefficient `_uuid`, `centrality`
``````algo(clustering_coefficient).params().stream() as lcc
where lcc.centrality == 1
return count(lcc)
``````

Results: 2