# Community detection for dynamic networks.

### Description

Dynamic network clustering/community detection using a latent space approach. Using temporal edge data, network actors are embedded onto a hypersphere and grouped based on direction.

### Details

Package: | dnc |

Type: | Package |

Version: | 1.0 |

Date: | 2016-05-05 |

License: | GPL (>= 2) |

This package can perform community detection on dynamic (temporal) network data observed at discrete time points. Communities are assumed fixed, but community membership may change. The main function is `dnc(...)`

which can perform variational Bayes estimation or alternatively implement a Gibbs sampler. A `dnc`

object is the output, for which there exists the following generic commands: `simulate()`

, `plot()`

, `print()`

, and `BIC()`

. Ignorable (MAR, MCAR) missing edge data can be incorporated into the Gibbs sampler.

### Author(s)

Daniel K. Sewell

Maintainer: Daniel K. Sewell <daniel-sewell@uiowa.edu>

### References

Sewell, D. K., and Chen, Y. (2016). Latent Space Approaches to Community Detection in Dynamic Networks. Bayesian Analysis. doi: 10.1214/16-BA1000. http://projecteuclid.org/euclid.ba/1461603847

### Examples

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