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This function finds communities in a (un)weighted undirected network based on leading eigen vector of the community matrix.


  weight = TRUE,
  cut_weight = 0,
  index = names(net)[3],
  bipartite = FALSE,
  site_col = 1,
  species_col = 2,
  return_node_type = "both",
  algorithm_in_output = TRUE



the output object from similarity() or dissimilarity_to_similarity(). If a data.frame is used, the first two columns represent pairs of sites (or any pair of nodes), and the next column(s) are the similarity indices.


a boolean indicating if the weights should be considered if there are more than two columns.


a minimal weight value. If weight is TRUE, the links between sites with a weight strictly lower than this value will not be considered (O by default).


name or number of the column to use as weight. By default, the third column name of net is used.


a boolean indicating if the network is bipartite (see Details).


name or number for the column of site nodes (i.e. primary nodes).


name or number for the column of species nodes (i.e. feature nodes).


a character indicating what types of nodes (site, species or both) should be returned in the output (return_node_type = "both" by default).


a boolean indicating if the original output of cluster_leading_eigen should be returned in the output (TRUE by default, see Value).


A list of class bioregion.clusters with five slots:

  1. name: character containing the name of the algorithm

  2. args: list of input arguments as provided by the user

  3. inputs: list of characteristics of the clustering process

  4. algorithm: list of all objects associated with the clustering procedure, such as original cluster objects (only if algorithm_in_output = TRUE)

  5. clusters: data.frame containing the clustering results

In the algorithm slot, if algorithm_in_output = TRUE, users can find the output of cluster_leading_eigen.


This function is based on leading eigenvector of the community matrix (Newman 2006) as implemented in the igraph package (cluster_leading_eigen).


Although this algorithm was not primarily designed to deal with bipartite network, it is possible to consider the bipartite network as unipartite network (bipartite = TRUE).

Do not forget to indicate which of the first two columns is dedicated to the site nodes (i.e. primary nodes) and species nodes (i.e. feature nodes) using the arguments site_col and species_col. The type of nodes returned in the output can be chosen with the argument return_node_type equal to both to keep both types of nodes, sites to preserve only the sites nodes and species to preserve only the species nodes.


Newman MEJ (2006). “Finding community structure in networks using the eigenvectors of matrices.” Physical Review E, 74(3), 036104.


Maxime Lenormand (, Pierre Denelle ( and Boris Leroy (


comat <- matrix(sample(1000, 50), 5, 10)
rownames(comat) <- paste0("Site", 1:5)
colnames(comat) <- paste0("Species", 1:10)

net <- similarity(comat, metric = "Simpson")
com <- netclu_leadingeigen(net)

net_bip <- mat_to_net(comat, weight = TRUE)
clust2 <- netclu_leadingeigen(net_bip, bipartite = TRUE)