@ARTICLE\{IMM2012-06319,
    author       = "M. M{\o}rup and M. N. Schmidt",
    title        = "Bayesian Community Detection",
    year         = "2012",
    journal      = "Neural Computation",
    volume       = "",
    editor       = "",
    number       = "",
    publisher    = "",
    url          = "http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00314?journalCode=neco",
    abstract     = "Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled.

Note: This publication comes with an errata: M{\o}rup, Morten, and Mikkel N. Schmidt. Errata to Bayesian Community Detection, Neural computation 26(6) pp. 1236-1237, 2014.
The  errata to this paper is also available at:  http://www2.imm.dtu.dk/pubdb/views/edoc\_download.php/6722/pdf/imm6722.pdf"
}