cluster_edge_betweenness {igraph} R Documentation

## Community structure detection based on edge betweenness

### Description

Many networks consist of modules which are densely connected themselves but sparsely connected to other modules.

### Usage

cluster_edge_betweenness(
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.

### Details

The edge betweenness score of an edge measures the number of shortest paths through it, see edge_betweenness() for details. The idea of the edge betweenness based community structure detection is that it is likely that edges connecting separate modules have high edge betweenness as all the shortest paths from one module to another must traverse through them. So if we gradually remove the edge with the highest edge betweenness score we will get a hierarchical map, a rooted tree, called a dendrogram of the graph. The leafs of the tree are the individual vertices and the root of the tree represents the whole graph.

cluster_edge_betweenness() performs this algorithm by calculating the edge betweenness of the graph, removing the edge with the highest edge betweenness score, then recalculating edge betweenness of the edges and again removing the one with the highest score, etc.

edge.betweeness.community returns various information collected through the run of the algorithm. See the return value down here.

### Value

cluster_edge_betweenness() returns a communities() object, please see the communities() manual page for details.

### Author(s)

Gabor Csardi csardi.gabor@gmail.com

### References

M Newman and M Girvan: Finding and evaluating community structure in networks, Physical Review E 69, 026113 (2004)

edge_betweenness() for the definition and calculation of the edge betweenness, cluster_walktrap(), cluster_fast_greedy(), cluster_leading_eigen() for other community detection methods.

See communities() for extracting the results of the community detection.

Community detection as_membership(), cluster_fast_greedy(), cluster_fluid_communities(), cluster_infomap(), cluster_label_prop(), cluster_leading_eigen(), cluster_leiden(), cluster_louvain(), cluster_optimal(), cluster_spinglass(), cluster_walktrap(), compare(), groups(), make_clusters(), membership(), modularity.igraph(), plot_dendrogram(), split_join_distance()

### Examples


g <- sample_pa(100, m = 2, directed = FALSE)
eb <- cluster_edge_betweenness(g)

g <- make_full_graph(10) %du% make_full_graph(10)