# modularity

##### Modularity of a community structure of a graph

This function calculates how modular is a given division of a graph into subgraphs.

- Keywords
- graphs

##### Usage

`modularity(graph, membership, weights = NULL)`

##### Arguments

- graph
- The input graph.
- membership
- Numeric vector, for each vertex it gives its community. The communities are numbered from zero.
- weights
- If not
`NULL`

then a numeric vector giving edge weights.

##### Details

The modularity of a graph with respect to some division (or vertex types) measures how good the division is, or how separated are the different vertex types from each other. It defined as $$Q=\frac{1}{2m} \sum_{i,j} A_{ij}-\frac{k_ik_j}{2m}\delta(c_i,c_j),$$ here $m$ is the number of edges, $A_{ij}$ is the element of the $A$ adjacency matrix in row $i$ and column $j$, $k_i$ is the degree of $i$, $k_j$ is the degree of $j$, $c_i$ is the type (or component) of $i$, $c_j$ that of $j$, the sum goes over all $i$ and $j$ pairs of vertices, and $\delta(x,y)$ is 1 if $x=y$ and 0 otherwise.

If edge weights are given, then these are considered as the element of the $A$ adjacency matrix, and $k_i$ is the sum of weights of adjacent edges for vertex $i$.

##### Value

- A numeric scalar, the modularity score of the given configuration.

##### concept

Modularity

##### References

MEJ Newman and M Girvan: Finding and evaluating community structure in networks. Physical Review E 69 026113, 2004.

##### See Also

`walktrap.community`

,
`edge.betweenness.community`

,
`fastgreedy.community`

,
`spinglass.community`

for various community detection
methods.

##### Examples

```
g <- graph.full(5) %du% graph.full(5) %du% graph.full(5)
g <- add.edges(g, c(0,5, 0,10, 5, 10))
wtc <- walktrap.community(g)
memb <- community.to.membership(g, wtc$merges, steps=12)
modularity(g, memb$membership)
```

*Documentation reproduced from package igraph, version 0.5.5-3, License: GPL (>= 2)*