This is a fast, nearly linear time algorithm for detecting community structure in networks. In works by labeling the vertices with unique labels and then updating the labels by majority voting in the neighborhood of the vertex.

`cluster_label_prop(graph, weights = NULL, initial = NULL, fixed = NULL)`

graph

The input graph, should be undirected to make sense.

weights

An optional weight vector. It should contain a positive
weight for all the edges. The ‘weight’ edge attribute is used if
present. Supply ‘`NA`

’ here if you want to ignore the
‘weight’ edge attribute. Larger edge weights correspond to
stronger connections.

initial

The initial state. If `NULL`

, every vertex will have a
different label at the beginning. Otherwise it must be a vector with an
entry for each vertex. Non-negative values denote different labels, negative
entries denote vertices without labels.

fixed

Logical vector denoting which labels are fixed. Of course this makes sense only if you provided an initial state, otherwise this element will be ignored. Also note that vertices without labels cannot be fixed.

`cluster_label_prop`

returns a
`communities`

object, please see the `communities`

manual page for details.

This function implements the community detection method described in: Raghavan, U.N. and Albert, R. and Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76, 036106. (2007). This version extends the original method by the ability to take edge weights into consideration and also by allowing some labels to be fixed.

From the abstract of the paper: “In our algorithm every node is initialized with a unique label and at every step each node adopts the label that most of its neighbors currently have. In this iterative process densely connected groups of nodes form a consensus on a unique label to form communities.”

Raghavan, U.N. and Albert, R. and Kumara, S.: Near linear time
algorithm to detect community structures in large-scale networks. *Phys
Rev E* 76, 036106. (2007)

`communities`

for extracting the actual results.

`cluster_fast_greedy`

, `cluster_walktrap`

and
`cluster_spinglass`

for other community detection methods.

```
# NOT RUN {
g <- sample_gnp(10, 5/10) %du% sample_gnp(9, 5/9)
g <- add_edges(g, c(1, 12))
cluster_label_prop(g)
# }
```

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