Description Usage Arguments Details Author(s) References Examples
Alluvial plot showing the dynamics of the group memberships associated to a dynamic stochastic block model.
1 | alluvial.plot(dynsbm, timestep.abbrev="T", minimal.flow=1, only.present=FALSE)
|
dynsbm |
An object of class |
timestep.abbrev |
Time step abbreviation (D fors days, W for weeks,...). |
minimal.flow |
Minimal flow to be displayed, i.e. minimal number of nodes to consider. |
only.present |
Enable/disable the appearence of the group O which gathers absent nodes. |
Alluvial flows between groups.
Between any two time plots (on the x-axis), lines are drawn to represent flows between groups.
Each flow corresponds to the membership overlapp between one a group to another group at two consecutive time steps (represented on the y-axis).
Each group q at each time step t is called Xt_q where X is time step abbreviation set with timestep.abbrev
.
The thickness of each line is proportional to the corresponding node counts.
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 switches between groups
alluvial.plot(dynsbm)
####################
## 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 switches between groups
alluvial.plot(dynsbm)
####################
## 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 switches between groups
alluvial.plot(dynsbm)
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