Description Usage Arguments Details Author(s) References Examples

Plot the connectivity characteristics between groups associated to a dynamic stochastic block model.

1 |

`dynsbm` |
An object of class |

`Y` |
An object of class |

Interaction presence and intensity between nodes in any of the groups to the others are represented in a QxQ matrix.
The cell in line q/column l deals with the connectivity between groups q/l.
Each cell contains a curve with T time points on the x-axis corresponding to the T proportions of present edges over all the possible edges, where Q is the number of groups and T is the number of time points, and
If `dynsbm`

was estimated with `edge.type=="binary"`

, the area below the curve is filled in light blue.
If `dynsbm`

was estimated with `edge.type=="discrete"`

, the area below the curve is divided into K areas corresponding to the proportion of edges with value 1 to K (the darker blue, the greater edge intensity).
If `dynsbm`

was estimated with `edge.type=="continuous"`

, the area below the curve is filled with a colored gradient representing the mean edge intensity (the darker blue, the greater).

Authors: Catherine Matias, Vincent Miele

Maintainer: Vincent Miele <vincent.miele@univ-lyon1.fr>

Catherine Matias and Vincent Miele, *Statistical clustering of temporal networks through a dynamic stochastic block model*, Journal of the Royal Statistical Society: Series B (2017)
http://dx.doi.org/10.1111/rssb.12200
http://arxiv.org/abs/1506.07464

Vincent Miele and Catherine Matias, *Revealing the hidden structure
of dynamic ecological networks*, Royal Society Open Science (2017)
http://dx.doi.org/10.1098/rsos.170251
https://arxiv.org/abs/1701.01355

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####################
## 1 - binary case
data(simdataT5Q4N40binary)
## estimation for Q=1..5 groups
list.dynsbm <- select.dynsbm(simdataT5Q4N40binary,
Qmin=1, Qmax=5, edge.type="binary", nstart=1)
## Not run:
## better to use nstart>1 starting points
## but estimation can take 1-2 minutes
list.dynsbm <- select.dynsbm(simdataT5Q4N40binary,
Qmin=1, Qmax=5, edge.type="binary", nstart=25)
## End(Not run)
## selection of Q=4
dynsbm <- list.dynsbm[[4]]
## plotting intra/inter connectivity patterns
connectivity.plot(dynsbm, simdataT5Q4N40binary)
####################
## 2 - continuous case
data(simdataT5Q4N40continuous)
## estimation for Q=1..5 groups
list.dynsbm <- select.dynsbm(simdataT5Q4N40continuous,
Qmin=1, Qmax=5, edge.type="continuous", nstart=1)
## Not run:
## better to use nstart>1 starting points
## but estimation can take 1-2 minutes
list.dynsbm <- select.dynsbm(simdataT5Q4N40continuous,
Qmin=1, Qmax=5, edge.type="continuous", nstart=25)
## End(Not run)
## selection of Q=4
dynsbm <- list.dynsbm[[4]]
## plotting intra/inter connectivity patterns
connectivity.plot(dynsbm, simdataT5Q4N40continuous)
####################
## 3 - discrete case
data(simdataT5Q4N40discrete)
## estimation for Q=1..5 groups
list.dynsbm <- select.dynsbm(simdataT5Q4N40discrete,
Qmin=1, Qmax=5, edge.type="discrete", K=4, nstart=1)
## Not run:
## better to use nstart>1 starting points
## but estimation can take 1-2 minutes
list.dynsbm <- select.dynsbm(simdataT5Q4N40discrete,
Qmin=1, Qmax=5, edge.type="discrete", K=4, nstart=25)
## End(Not run)
## selection of Q=4
dynsbm <- list.dynsbm[[4]]
## plotting intra/inter connectivity patterns
connectivity.plot(dynsbm, simdataT5Q4N40discrete)
``` |

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