getEnsList {DCG} | R Documentation |
getEnsList
get ensemble matrices from given similarity matrix at all temperatures
getEnsList(simMat, temperatures, MaxIt = 1000, m = 5)
simMat |
a similarity matrix |
temperatures |
temperatures selected |
MaxIt |
number of iterations for regulated random walks |
m |
maxiumnum number of time a node can be visited during random walks |
This step is crucial in finding community structure based on the similarity matrix of the social network.
For each temperatures
, the similarity matrix was taken to the power of temperature
as saved as a new similarity matrix.
This allows the random walk to explore the similarity matrix at various variations.
Random walks are then performed in similarity matrices of various temperatures.
In order to prevent random walks being stucked in a locale, the parameter m
was set (to 5
by default) to remove a node after m
times of visits of the node.
An ensemble matrix is generated at each temperature in which values represent likelihood of two nodes being in the same community.
a list of ensemble matrices
Fushing, H., & McAssey, M. P. (2010). Time, temperature, and data cloud geometry. Physical Review E, 82(6), 061110.
Chen, C., & Fushing, H. (2012). Multiscale community geometry in a network and its application. Physical Review E, 86(4), 041120.
Fushing, H., Wang, H., VanderWaal, K., McCowan, B., & Koehl, P. (2013). Multi-scale clustering by building a robust and self correcting ultrametric topology on data points. PloS one, 8(2), e56259.
symmetricMatrix <- as.symmetricAdjacencyMatrix(monkeyGrooming, weighted = TRUE, rule = "weak")
Sim <- as.SimilarityMatrix(symmetricMatrix)
temperatures <- temperatureSample(start = 0.01, end = 20, n = 20, method = 'random')
## Not run:
# Note: It takes a while to run the getEnsList example.
Ens_list <- getEnsList(Sim, temperatures, MaxIt = 1000, m = 5)
## End(Not run)