dynsbm (version 0.7)

alluvial.plot: Alluvial plot showing the dynamics of the group memberships associated to a dynamic stochastic block model.

Description

Alluvial plot showing the dynamics of the group memberships associated to a dynamic stochastic block model.

Usage

alluvial.plot(dynsbm, timestep.abbrev="T", minimal.flow=1, only.present=FALSE)

Arguments

dynsbm

An object of class dynsbm retrieved with the function select.dynsbm.

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.

Details

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.

References

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

Examples

# NOT RUN {
####################
## 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)
# }
# 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)
# }
# 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)
# }
# NOT RUN {
## selection of Q=4
dynsbm <- list.dynsbm[[4]]

## plotting switches between groups
alluvial.plot(dynsbm)
# }