Community structure detection based on edge betweenness

edge.betweenness.community() was renamed to cluster_edge_betweenness() to create a more consistent API.

Usage

edge.betweenness.community(
  graph,
  weights = NULL,
  directed = TRUE,
  edge.betweenness = TRUE,
  merges = TRUE,
  bridges = TRUE,
  modularity = TRUE,
  membership = TRUE
)

Arguments

graph

The graph to analyze.

weights

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. Edge weights are used to calculate weighted edge betweenness. This means that edges are interpreted as distances, not as connection strengths.

directed

Logical constant, whether to calculate directed edge betweenness for directed graphs. It is ignored for undirected graphs.

edge.betweenness

Logical constant, whether to return the edge betweenness of the edges at the time of their removal.

merges

Logical constant, whether to return the merge matrix representing the hierarchical community structure of the network. This argument is called merges, even if the community structure algorithm itself is divisive and not agglomerative: it builds the tree from top to bottom. There is one line for each merge (i.e. split) in matrix, the first line is the first merge (last split). The communities are identified by integer number starting from one. Community ids smaller than or equal to \(N\), the number of vertices in the graph, belong to singleton communities, i.e. individual vertices. Before the first merge we have \(N\) communities numbered from one to \(N\). The first merge, the first line of the matrix creates community \(N+1\), the second merge creates community \(N+2\), etc.

bridges

Logical constant, whether to return a list the edge removals which actually splitted a component of the graph.

modularity

Logical constant, whether to calculate the maximum modularity score, considering all possibly community structures along the edge-betweenness based edge removals.

membership

Logical constant, whether to calculate the membership vector corresponding to the highest possible modularity score.