mixer {mixer} | R Documentation |
Estimate the parameters and hidden class variable of a Mixture of Erdös Rényi Random Graphs. The estimation is performed for a Bernoulli Mixture
mixer(x,qmin=2,qmax=NULL,method="variational",nbiter=10,improve=FALSE)
x |
Adjacency matrix or a matrix of egdes (each column gives the nodes defining an edge) or spm filename (a .spm file describes the network as a sparse matrix |
qmin |
Number of classes estimated (if used alone, it is the minimum number of classes which will be tested) |
qmax |
Maximimum number of classes (use with qmin) |
method |
strategy used for the estimation: "classification", "variational", or "bayesian" |
nbiter |
Number of EM iterations for the E step (Default: 10) |
improve |
Flag to choose between improved or basic strategies for chosing the model |
mixer
is a R wrapper for the c++ software package mixnet
developped by Vincent Miele (2006).
Erdös-Rényi Mixture Model for Graph (MixNet), which has been proposed by Daudin et. al (2008) with an associated EM estimation algorithm, and is not to be confused with Exponential Random Graph Models for Network Data (ERGM) which consider distributions ensuing from the exponential family to model the edge distribution. The MixNet model allows to capture the structure of a network and in particular to detect communities.
There exists a strong connection between Mixnet and block clustering.. Block clustering searches for homogeneous blocks in a data matrix by simultaneous clustering of rows and columns.
The proposed estimation strategies deals with undirected graphs. They are of two type:
mixer
returns an object of class mixer, which is basically a list of item,
each item containing the result of the estimation for a given number
of class q. It has the following fields
ICL |
Integrated Classification Likelihood, which is the criterion used for model selection |
alphas |
The vector of proportion, whose length is the number of component |
Pis |
The connectivity matrix |
Taus |
The matrix of posterior probabilities (of the hidden color knowing the graph structure) |
Christophe Ambroise
Jean-Jacques Daudin, Franck Picard and Stephane Robin June (2008) , A mixture model for random graphs. Statistics and Computing, 18, 2, 151–171.
Hugo Zanghi, Christophe Ambroise and Vincent Miele (2008), Fast online graph clustering via Erdös-Rényi mixture. Pattern Recognition, 41, 3592-3599.
Hugo Zanghi, Franck Picard, Vincent Miele, and Christophe Ambroise (2008), Strategies for Online Inference of Network Mixture, http://genome.jouy.inra.fr/ssb/preprint/AMPZ07-SSBpreprint.pdf
Pierre Latouche, Etienne Birmele, and Christophe Ambroise (2008), Bayesian methods for graph clustering, http://genome.jouy.inra.fr/ssb/preprint/SSB-RR-17.bayesianMixNet.pdf
Vincent Miele. MixNet C++ package, http://stat.genopole.cnrs.fr/sg/software/mixer/.
graph.affiliation(n=100,c(1/3,1/3,1/3),0.8,0.2)->g mixer(g$x,qmin=2,qmax=6)->xout ## Not run: plot(xout) graph.affiliation(n=50,c(1/3,1/3,1/3),0.8,0.2)->g mixer(g$x,qmin=2,qmax=5, method="bayesian")->xout ## Not run: plot(xout) ## Not run: plot(xout) data(blog) mixer(x=blog$links,qmin=2,qmax=12)->xout ## Not run: plot(xout)