leading.eigenvector.community {igraph} | R Documentation |
These functions try to find densely connected subgraphs in a graph by calculating the leading non-negative eigenvector of the modularity matrix of the graph.
leading.eigenvector.community(graph, steps = -1, options = igraph.arpack.default) leading.eigenvector.community.naive(graph, steps = -1, options = igraph.arpack.default) leading.eigenvector.community.step (graph, fromhere = NULL, membership = rep(0, vcount(graph)), community = 0, options=igraph.arpack.default) community.le.to.membership(merges, steps, membership)
graph |
The input graph. Should be undirected as the method needs a symmetric matrix. |
steps |
The number of steps to take, this is actually the number of tries to make a step. It is not a particularly useful parameter. For |
naive |
Logical constant, it defines how the algorithm tries to
find more divisions after the first division was made. If
|
fromhere |
An object returned by a previous call to
|
membership |
The starting community structure. It is a numeric
vector defining the membership of every vertex in the graph with a
number between 0 and the total number of communities at this level
minus one. By default we start with a single community containing
all vertices. This argument is ignored if For |
community |
The id of the community which the algorithm will try to split. |
options |
A named list to override some ARPACK options. |
merges |
The merge matrix, possible from the result of
|
The functions documented in these section implement the ‘leading eigenvector’ method developed by Mark Newman and published in MEJ Newman: Finding community structure using the eigenvectors of matrices, arXiv:physics/0605087. TODO: proper citation.
The heart of the method is the definition of the modularity matrix,
B
, which is B=A-P
, A
being the adjacency matrix of
the (undirected)
network, and P
contains the probability that certain edges are
present according to the ‘configuration model’. In
other words, a P[i,j]
element of P
is the probability that
there is an edge between vertices i
and j
in a random
network in which the degrees of all vertices are the same as in the
input graph.
The leading eigenvector method works by calculating the eigenvector of the modularity matrix for the largest positive eigenvalue and then separating vertices into two community based on the sign of the corresponding element in the eigenvector. If all elements in the eigenvector are of the same sign that means that the network has no underlying comuunity structure. Check Newman's paper to understand why this is a good method for detecting community structure.
leading.eigenvector.community
is the proper implementation of
the proposed algorithm. leading.eigenvector.community.naive
is
considered worse, in this implementation a community found after a
division is considered as a separate graph for further divisions.
leading.eigenvector.community.step
is the proper
implementation, but makes only one step, by trying to split the
specified community.
From igraph 0.5 these functions use ARPACK to calculate the
eigenvectors. See arpack
for details.
community.le.to.memberhip
creates a membership vector from the
result of leading.eigenvector.community
or
leading.eigenvector.community.naive
. It takes membership
and permformes steps
merges, according to the supplied
merges
matrix.
leading.eigenvector.community
and
leading.eigenvector.community.naive
return a named list with
the following members:
membership |
The membership vector at the end of the algorithm, when no more splits are possible. |
merges |
The merges matrix starting from the state described by
the |
options |
Information about the underlying ARPACK computation,
see |
leading.eigenvector.community.step
returns a named list with
the following members:
membership |
The new membership vector after the split. If no split was done then this is the same as the input membership vector. |
split |
Logical scalar, if |
eigenvector |
The eigenvector of the community matrix, or
|
eigenvalue |
The largest positive eigenvalue of the modularity matrix. |
options |
Information about the underlying ARPACK computation,
see |
community.le.to.membership
returns a named list with two
components:
membership |
A membership vector, a numerical vector indication which vertex belongs to which community. The communities are always numbered from zero. |
csize |
A numeric vector giving the sizes of the communities. |
Gabor Csardi csardi@rmki.kfki.hu
MEJ Newman: Finding community structure using the eigenvectors of matrices, arXiv:physics/0605087
modularity
, walktrap.community
,
edge.betweenness.community
,
fastgreedy.community
,
as.dendrogram
in package stats
.
g <- graph.full(5) %du% graph.full(5) %du% graph.full(5) g <- add.edges(g, c(0,5, 0,10, 5, 10)) leading.eigenvector.community(g) lec <- leading.eigenvector.community.step(g) lec$membership # Try one more split leading.eigenvector.community.step(g, fromhere=lec, community=0) leading.eigenvector.community.step(g, fromhere=lec, community=1)