spinglass.community {igraph} | R Documentation |
This function tries to find communities in graphs via a spin-glass model and simulated annealing.
spinglass.community(graph, weights=NULL, spins=25, parupdate=FALSE, start.temp=1, stop.temp=0.1, cool.fact=0.99, update.rule=c("config", "random", "simple"), gamma=1) spinglass.community(graph, weights=NULL, vertex, spins=25, update.rule=c("config", "random", "simple"), gamma=1)
graph |
The input graph, can be directed but the direction of the edges is neglected. |
weights |
The weights of the edges. Either a numeric vector or
NULL . If it is null and the input graph has a ‘weight’
edge attribute then that will be used. If NULL and no such
attribute is present then the edges will have equal weights. |
spins |
Integer constant, the number of spins to use. This is the upper limit for the number of communities. It is not a problem to supply a (reasonably) big number here, in which case some spin states will be unpopulated. |
parupdate |
Logical constant, whether to update the spins of the
vertices in parallel (synchronously) or not. This argument is
ignored if the second form of the function is used (ie. the
‘vertex ’ argument is present). |
start.temp |
Real constant, the start temperature.
This argument is ignored if the second form of the
function is used (ie. the ‘vertex ’ argument is
present). |
stop.temp |
Real constant, the stop temperature. The simulation
terminates if the temperature lowers below this level.
This argument is ignored if the second form of the
function is used (ie. the ‘vertex ’ argument is
present). |
cool.fact |
Cooling factor for the simulated annealing.
This argument is ignored if the second form of the
function is used (ie. the ‘vertex ’ argument is
present). |
update.rule |
Character constant giving the ‘null-model’ of the simulation. Possible values: “simple” and “config”. “simple” uses a random graph with the same number of edges as the baseline probability and “config” uses a random graph with the same vertex degrees as the input graph. |
gamma |
Real constant, the gamma argument of the algorithm. This specifies the balance between the importance of present and non-present edges in a community. Roughly, a comunity is a set of vertices having many edges inside the community and few edges outside the community. The default 1.0 value makes existing and non-existing links equally important. Smaller values make the existing links, greater values the missing links more important. |
vertex |
This parameter can be used to calculate the community of a given vertex without calculating all communities. Note that if this argument is present then some other arguments are ignored. |
This function tries to find communities in a graph. A community is a set of nodes with many edges inside the community and few edges between outside it (ie. between the community itself and the rest of the graph.
If the vertex
argument is not given, ie. the first form is used
then a named list is returned with the following slots:
membership |
Integer vector giving the communities found. The communities have ids starting from zero and for each graph vertex ids community id is given in this vector. |
csize |
The sizes of the communities in the order of their ids. |
modularity |
The modularity score of the result, as defined by Newman and Girvan, see references. |
temperature |
The temperature of the system when the algorithm terminated. |
community |
Numeric vector giving the ids of the vertices in
the same community as vertex . |
cohesion |
The cohesion score of the result, see references. |
adhesion |
The adhesion score of the result, see references. |
inner.links |
The number of edges within the community of
vertex . |
outer.links |
The number of edges between the community of
vertex and the rest of the graph. |
Jorg Reichardt lastname@physik.uni-wuerzburg.de for the original code and Gabor Csardi csardi@rmki.kfki.hu for the igraph glue code
J. Reichardt and S. Bornholdt: Statistical Mechanics of Community Detection, Phys. Rev. E, 74, 016110 (2006), http://arxiv.org/abs/cond-mat/0603718
M. E. J. Newman and M. Girvan: Finding and evaluating community structure in networks, Phys. Rev. E 69, 026113 (2004)
g <- erdos.renyi.game(10, 5/10) %du% erdos.renyi.game(9, 5/9) g <- add.edges(g, c(0, 11)) g <- subgraph(g, subcomponent(g, 0)) spinglass.community(g, spins=2) spinglass.community(g, vertex=0)