connectivity.plot: Plot the connectivity characteristics between groups...

In dynsbm: Dynamic Stochastic Block Models

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

Description

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

Usage

connectivity.plot(dynsbm, Y)

Arguments

dynsbm	An object of class dynsbm retrieved with the function select.dynsbm.
Y	An object of class array of dimension (T x N x N) containing T adjacency matrices of size (N x N), where N is the number of nodes in the network and T is the number of time points.

Details

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).

Value

No return value, called for plotting.

Author(s)

Authors: Catherine Matias, Vincent Miele

Maintainer: Vincent Miele <[e-mail]>

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

####################
## 1 - binary case
data(simdataT5Q4N40binary)

## estimation for Q=1..5 groups
## 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=1)
				
## 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
## 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=1)
						
## 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
## 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=1)
									
## selection of Q=4
dynsbm <- list.dynsbm[[4]]

## plotting intra/inter connectivity patterns
connectivity.plot(dynsbm, simdataT5Q4N40discrete)

dynsbm documentation built on June 8, 2025, 11 a.m.