# R igraph manual pages

Use this if you are using igraph from R

## Modularity of a community structure of a graph

### Description

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

### Usage

```## S3 method for class 'igraph'
modularity(x, membership, weights = NULL, resolution = 1, directed = TRUE, ...)

modularity_matrix(
graph,
membership,
weights = NULL,
resolution = 1,
directed = TRUE
)
```

### Arguments

 `x, graph` The input graph. `membership` Numeric vector, one value for each vertex, the membership vector of the community structure. `weights` If not `NULL` then a numeric vector giving edge weights. `resolution` The resolution parameter. Must be greater than or equal to 0. Set it to 1 to use the classical definition of modularity. `directed` Whether to use the directed or undirected version of modularity. Ignored for undirected graphs. `...` Additional arguments, none currently.

### Details

`modularity` calculates the modularity of a graph with respect to the given `membership` vector.

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=1/(2m) * sum( (Aij-gamma*ki*kj/(2m) ) delta(ci,cj),i,j),

here m is the number of edges, Aij is the element of the A adjacency matrix in row i and column j, ki is the degree of i, kj is the degree of j, ci is the type (or component) of i, cj 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.

The resolution parameter gamma allows weighting the random null model, which might be useful when finding partitions with a high modularity. Maximizing modularity with higher values of the resolution parameter typically results in more, smaller clusters when finding partitions with a high modularity. Lower values typically results in fewer, larger clusters. The original definition of modularity is retrieved when setting gamma to 1.

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

`modularity_matrix` calculates the modularity matrix. This is a dense matrix, and it is defined as the difference of the adjacency matrix and the configuration model null model matrix. In other words element M[i,j] is given as A[i,j]-d[i]d[j]/(2m), where A[i,j] is the (possibly weighted) adjacency matrix, d[i] is the degree of vertex i, and m is the number of edges (or the total weights in the graph, if it is weighed).

### Value

For `modularity` a numeric scalar, the modularity score of the given configuration.

For `modularity_matrix` a numeric square matrix, its order is the number of vertices in the graph.

### Author(s)

Gabor Csardi csardi.gabor@gmail.com

### References

Clauset, A.; Newman, M. E. J. & Moore, C. Finding community structure in very large networks, Physical Review E 2004, 70, 066111

`cluster_walktrap`, `cluster_edge_betweenness`, `cluster_fast_greedy`, `cluster_spinglass`, `cluster_louvain` and `cluster_leiden` for various community detection methods.

### Examples

```
g <- make_full_graph(5) %du% make_full_graph(5) %du% make_full_graph(5)
g <- add_edges(g, c(1,6, 1,11, 6, 11))
wtc <- cluster_walktrap(g)
modularity(wtc)
modularity(g, membership(wtc))

```

[Package igraph version 1.3.0 Index]