R igraph manual pages

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cluster_leiden {igraph}R Documentation

Finding community structure of a graph using the Leiden algorithm of Traag, van Eck & Waltman.


Finding community structure of a graph using the Leiden algorithm of Traag, van Eck & Waltman.


  objective_function = c("CPM", "modularity"),
  weights = NULL,
  resolution_parameter = 1,
  beta = 0.01,
  initial_membership = NULL,
  n_iterations = 2,
  vertex_weights = NULL



The input graph, only undirected graphs are supported.


Whether to use the Constant Potts Model (CPM) or modularity. Must be either "CPM" or "modularity".


The weights of the edges. It must be a positive numeric vector, NULL or NA. If it is NULL and the input graph has a ‘weight’ edge attribute, then that attribute will be used. If NULL and no such attribute is present, then the edges will have equal weights. Set this to NA if the graph was a ‘weight’ edge attribute, but you don't want to use it for community detection. A larger edge weight means a stronger connection for this function.


The resolution parameter to use. Higher resolutions lead to more smaller communities, while lower resolutions lead to fewer larger communities.


Parameter affecting the randomness in the Leiden algorithm. This affects only the refinement step of the algorithm.


If provided, the Leiden algorithm will try to improve this provided membership. If no argument is provided, the aglorithm simply starts from the singleton partition.


the number of iterations to iterate the Leiden algorithm. Each iteration may improve the partition further.


the vertex weights used in the Leiden algorithm. If this is not provided, it will be automatically determined on the basis of whether you want to use CPM or modularity. If you do provide this, please make sure that you understand what you are doing.


cluster_leiden returns a communities object, please see the communities manual page for details.


Vincent Traag


Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports, 9(1), 5233. doi: 10.1038/s41598-019-41695-z

See Also

See communities for extracting the membership, modularity scores, etc. from the results.

Other community detection algorithms: cluster_walktrap, cluster_spinglass, cluster_leading_eigen, cluster_edge_betweenness, cluster_fast_greedy, cluster_label_prop cluster_louvain


g <- graph.famous("Zachary")
# By default CPM is used
g <- cluster_leiden(g, resolution_parameter=0.06)

[Package igraph version 1.3.1 Index]