Community detection for dynamic networks.
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.
|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:
BIC(). Ignorable (MAR, MCAR) missing edge data can be incorporated into the Gibbs sampler.
Daniel K. Sewell
Maintainer: Daniel K. Sewell <firstname.lastname@example.org>
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
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