Bayesian community detection.

Publication: Research - peer-reviewJournal article – Annual report year: 2012

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Bayesian community detection.. / Mørup, Morten; Schmidt, Mikkel N.

In: Neural Computation, Vol. 24, No. 9, 2012, p. 2434-2456.

Publication: Research - peer-reviewJournal article – Annual report year: 2012

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Author

Mørup, Morten; Schmidt, Mikkel N / Bayesian community detection..

In: Neural Computation, Vol. 24, No. 9, 2012, p. 2434-2456.

Publication: Research - peer-reviewJournal article – Annual report year: 2012

Bibtex

@article{7f39116baeb9487ab6f013f50e0c7e85,
title = "Bayesian community detection.",
keywords = "Community detection, Complex networks, Infinite relational Model, Stochastic Block-model, Modularity, Normalized Cut",
publisher = "M I T Press",
author = "Morten Mørup and Schmidt, {Mikkel N}",
year = "2012",
doi = "10.1162/NECO_a_00314",
volume = "24",
number = "9",
pages = "2434--2456",
journal = "Neural Computation",
issn = "0899-7667",

}

RIS

TY - JOUR

T1 - Bayesian community detection.

A1 - Mørup,Morten

A1 - Schmidt,Mikkel N

AU - Mørup,Morten

AU - Schmidt,Mikkel N

PB - M I T Press

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Community detection

KW - Complex networks

KW - Infinite relational Model

KW - Stochastic Block-model

KW - Modularity

KW - Normalized Cut

U2 - 10.1162/NECO_a_00314

DO - 10.1162/NECO_a_00314

JO - Neural Computation

JF - Neural Computation

SN - 0899-7667

IS - 9

VL - 24

SP - 2434

EP - 2456

ER -