# label.propagation.community

0th

Percentile

##### Finding communities based on propagating labels

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.

Keywords
graphs
##### Usage
label.propagation.community (graph, weights = NULL,
initial = NULL, fixed = NULL)
##### Arguments
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 edg
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 with
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.
##### 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.

##### Value

• A numeric vector giving the final label (=community) of each vertex.

##### concept

Community structure

##### References

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)

fastgreedy.community, walktrap.community and spinglass.community for other community detection methods.

##### Aliases
• label.propagation.community
##### Examples
g <- erdos.renyi.game(10, 5/10) %du% erdos.renyi.game(9, 5/9)
g <- add.edges(g, c(0, 11))
label.propagation.community(g)
Documentation reproduced from package igraph, version 0.5.3, License: GPL (>= 2)

### Community examples

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