Community Detection

Detects communities in a network with one of the community detection algorithms of igraph. Each method calls the matching community_*() function.

Usage

communities(
  x,
  method = c("louvain", "leiden", "fast_greedy", "walktrap", "infomap",
    "label_propagation", "edge_betweenness", "leading_eigenvector", "spinglass",
    "optimal", "fluid"),
  community = NULL,
  weights = NULL,
  resolution = 1,
  directed = NULL,
  seed = NULL,
  ...
)

Arguments

x: Network input: matrix, igraph, network, CographNetwork, cograph_network, or tna object.
method: Community detection algorithm. One of "louvain" (default; Louvain modularity optimization), "leiden" (Leiden algorithm), "fast_greedy" (greedy modularity optimization), "walktrap" (random walks), "infomap" (map equation), "label_propagation" (label propagation), "edge_betweenness" (Girvan-Newman), "leading_eigenvector" (leading eigenvector of the modularity matrix), "spinglass" (spinglass model), "optimal" (exact modularity maximization) or "fluid" (fluid communities).
community: Optional integer or character vector. If supplied, the returned data frame is filtered to rows whose community column matches one of the given values. Default NULL (keep all communities).
weights: Edge weights. NULL (default) uses the edge weights of the network when present and otherwise runs unweighted. NA runs unweighted.
resolution: Resolution parameter of the louvain and leiden methods. For louvain, higher values yield more communities. Default 1.
directed: Logical. Whether the edge betweenness method treats the network as directed. NULL (default) uses the direction of the network. The other methods ignore it.
seed: Random seed for reproducibility. It applies to the stochastic methods (louvain, leiden, infomap, label_propagation, spinglass).
...: Additional arguments passed to the community_*() function of the chosen method, for example no.of.communities for "fluid".

Value

A cograph_communities data frame with one row per node and the columns

node: Node label (character).
community: Community number (numeric).

The attributes "algorithm" (method name), "modularity" (modularity of the partition, NA when igraph does not compute it), "network" (the input x) and "igraph_result" (the igraph communities object) hold the metadata. When community is supplied, only the matching rows are kept.

Details

The louvain, leiden, fast_greedy, leading_eigenvector and fluid methods require an undirected graph. For a directed input this function prints a message and runs "walktrap" instead. Called directly, community_louvain() and community_leiden() raise an igraph error on a directed graph, while community_fast_greedy(), community_leading_eigenvector() and community_fluid() collapse it to an undirected graph with summed weights.

Negative edge weights are replaced by their absolute values for all methods except spinglass and optimal, which receive the weights as they are.

Method	Typical use
louvain	Large undirected networks
leiden	Large undirected networks, well-connected communities
fast_greedy	Medium-sized networks, hierarchical merges
walktrap	Directed or undirected networks, hierarchical merges
infomap	Directed networks with flow structure
label_propagation	Very large networks
edge_betweenness	Small networks, hierarchical splits
leading_eigenvector	Undirected networks, hierarchical splits
spinglass	Small connected networks, negative weights
optimal	Networks of at most about 50 nodes
fluid	Connected networks with a known number of communities

Printing and plotting

Printing the result shows the algorithm, the number of nodes and communities, the modularity, the community sizes and the node table. The result is itself a data frame. plot() on the result is documented in plot-results.

Examples

communities(regulation_net, method = "walktrap")
#> Community structure (walktrap)
#>   Nodes: 10  | Communities: 2  | Modularity: 0.1976 
#>   Sizes: 5, 5 
#> 
#>        node community
#>     Explore         1
#>        Plan         2
#>     Monitor         2
#>       Adapt         1
#>     Reflect         1
#>     Discuss         1
#>  Synthesize         1
#>    Evaluate         2
#>      Create         2
#>       Share         2