slm.communities: smart local moving

In chen198328/slm: smart local moving (SLM) algorithm is an algorithm for community detection (or clustering) in large networks

Description

smart local moving (SLM) algorithm is an algorithm for community detection (or clustering) in large networks. The SLM algorithm maximizes a so-called modularity function. The algorithm has been successfully applied to networks with tens of millions of nodes and hundreds of millions of edges. The details of the algorithm are documented in a paper (Waltman & Van Eck, 2013)

Usage

slm.community(g, e.weight="weight", modularity = 1, resolution = 1, algorithm = 3, nrandom = 10, iterations = 10, randomseed = 0, print = 0)

Arguments

g	An igraph object as returned by igraph.
e.weight	Vertex Attribute Name of weight
modularity	Modularity function (1 = standard; 2 = alternative)
resolution	Value of the resolution parameter
algorithm	Algorithm for modularity optimization (1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm)
nrandom	Number of random starts
iterations	Number of iterations per random start
randomseed	Seed of the random number generator
print	Whether or not to print output to the console (0 = no; 1 = yes)
memory.size	When using VOSviewer with large amounts of data, the memory requirements may be substantial. If there is not enough memory available, an out of memory error will occur.
stack.size	When working with large amounts of data, it is also possible that a stack overflow error will occur. The stack size then needs to be increased.

Examples

library(igraph)
library(igraphdata)
data("karate")

slm<-slm.community(karate)
plot(slm,karate)

chen198328/slm documentation built on Aug. 8, 2021, 4:13 p.m.