cluster_edge_betweenness(graph, weights = E(graph)$weight, directed = TRUE,
edge.betweenness = TRUE, merges = TRUE, bridges = TRUE,
modularity = TRUE, membership = TRUE)
NULL
to omit edge weights. By
default the weight
merges
, even if the community structure algorithm
itself is divisive and not agglomerative: it buicluster_edge_betweenness
returns a
communities
object, please see the communities
manual page for details.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
throught the run of the algorithm. See the return value down here.
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.
g <- barabasi.game(100,m=2)
eb <- cluster_edge_betweenness(g)
g <- make_full_graph(10) %du% make_full_graph(10)
g <- add_edges(g, c(1,11))
eb <- cluster_edge_betweenness(g)
eb
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