igraph (version 1.0.1)

compare: Compares community structures using various metrics

Description

This function assesses the distance between two community structures.

Usage

compare(comm1, comm2, method = c("vi", "nmi", "split.join", "rand",
  "adjusted.rand"))

Arguments

comm1

A communities object containing a community structure; or a numeric vector, the membership vector of the first community structure. The membership vector should contain the community id of each vertex, the numbering of the communities starts with one.

comm2

A communities object containing a community structure; or a numeric vector, the membership vector of the second community structure, in the same format as for the previous argument.

method

Character scalar, the comparison method to use. Possible values: ‘vi’ is the variation of information (VI) metric of Meila (2003), ‘nmi’ is the normalized mutual information measure proposed by Danon et al. (2005), ‘split.join’ is the split-join distance of can Dongen (2000), ‘rand’ is the Rand index of Rand (1971), ‘adjusted.rand’ is the adjusted Rand index by Hubert and Arabie (1985).

Value

A real number.

References

Meila M: Comparing clusterings by the variation of information. In: Scholkopf B, Warmuth MK (eds.). Learning Theory and Kernel Machines: 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA. Lecture Notes in Computer Science, vol. 2777, Springer, 2003. ISBN: 978-3-540-40720-1.

Danon L, Diaz-Guilera A, Duch J, Arenas A: Comparing community structure identification. J Stat Mech P09008, 2005.

van Dongen S: Performance criteria for graph clustering and Markov cluster experiments. Technical Report INS-R0012, National Research Institute for Mathematics and Computer Science in the Netherlands, Amsterdam, May 2000.

Rand WM: Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846-850, 1971.

Hubert L and Arabie P: Comparing partitions. Journal of Classification 2:193-218, 1985.

See Also

cluster_walktrap, cluster_edge_betweenness, cluster_fast_greedy, cluster_spinglass for various community detection methods.

Examples

# NOT RUN {
g <- make_graph("Zachary")
sg <- cluster_spinglass(g)
le <- cluster_leading_eigen(g)
compare(sg, le, method="rand")
compare(membership(sg), membership(le))
# }