MixeR is a R wrapper for the c++ software package mixnet developped by Vincent Miele (2006) [Download].
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:
- variational: which refers to the paper of Daudin et. al (2008). It is the default method.
- classification: which implements the method described in Zanghi et. al (2008). This method is faster than the variational approach and is able to deal with bigger network but can produce biased estimates.
- bayesian which implements the method described in Latouche et. al (2008). It improves over variational and classification strategies when dealing with small networks (less than 50 nodes). It should not be used with networks having more than 100 nodes.