netseer
netseer 
The goal of netseer is to predict the graph structure including new nodes and edges from a time series of graphs. The methodology is explained in the preprint (Kandanaarachchi 2024). We will illustrate an example in this vignette.
Installation
You can install the development version of netseer from GitHub with:
# install.packages("devtools")
devtools::install_github("sevvandi/netseer")
An example
This is a basic example which shows you how to predict a graph at the next time point. First let us generate some graphs.
library(netseer)
library(igraph)
#>
#> Attaching package: 'igraph'
#> The following objects are masked from 'package:stats':
#>
#> decompose, spectrum
#> The following object is masked from 'package:base':
#>
#> union
set.seed(2024)
edge_increase_val <- new_nodes_val <- del_edge_val <- 0.1
graphlist <- list()
graphlist[[1]] <- gr <- igraph::sample_pa(5, directed = FALSE)
for(i in 2:15){
gr <- generate_graph(gr,
del_edge = del_edge_val,
new_nodes = new_nodes_val,
edge_increase = edge_increase_val )
graphlist[[i]] <- gr
}
The graphlist contains the list of graphs we generated. Each graph is an igraph object. Let’s plot a couple of them.
Plotting a couple of graphs
plot(graphlist[[1]])
plot(graphlist[[8]])
plot(graphlist[[15]])
###
Predicting the next graph
Let’s predict the next graph. The argument
specifies we want to predict the graph at the next time point.
grpred <- predict_graph(graphlist[1:15],h = 1)
#> Warning: 2 errors (1 unique) encountered for arima
#> [2] missing value where TRUE/FALSE needed
#> Joining with `by = join_by(vertex)`
#> Joining with `by = join_by(From, To)`
#> Joining with `by = join_by(original)`
#> Joining with `by = join_by(original)`
grpred
#> $graph_mean
#> IGRAPH 74e74fc U--- 34 24 --
#> + edges from 74e74fc:
#> [1] 1-- 2 1-- 4 1-- 5 1-- 6 1--11 1--14 1--25 2-- 3 2-- 4 2--17
#> [11] 2--21 3-- 5 3-- 6 3-- 9 3--17 4-- 7 5--10 5--12 9--20 10--11
#> [21] 11--19 13--15 17--19 30--34
#>
#> $graph_lower
#> NULL
#>
#> $graph_upper
#> NULL
plot(grpred$graph_mean)
ecount(grpred$graph_mean)
#> [1] 24
vcount(grpred$graph_mean)
#> [1] 34
Predicting the graph at 2 time steps ahead
Now let us predict the graph at 2 time steps ahead with
.
grpred2 <- predict_graph(graphlist[1:15], h = 2)
#> Warning: 2 errors (1 unique) encountered for arima
#> [2] missing value where TRUE/FALSE needed
#> Joining with `by = join_by(vertex)`
#> Joining with `by = join_by(From, To)`
#> Joining with `by = join_by(original)`
#> Joining with `by = join_by(original)`
grpred2
#> $graph_mean
#> IGRAPH 96a324d U--- 37 27 --
#> + edges from 96a324d:
#> [1] 1-- 2 1-- 4 1-- 5 1-- 6 1--11 1--14 1--25 2-- 3 2-- 4 2--17
#> [11] 2--21 3-- 5 3-- 6 3-- 9 3--17 4-- 7 5--10 5--12 9--20 10--11
#> [21] 11--19 13--15 17--19 30--34 30--35 30--36 30--37
#>
#> $graph_lower
#> NULL
#>
#> $graph_upper
#> NULL
plot(grpred2$graph_mean)
ecount(grpred2$graph_mean)
#> [1] 27
vcount(grpred2$graph_mean)
#> [1] 37
We see the predicted graph at
has more vertices and edges than the graph at
.
Predicting the graph at 3 time steps ahead
Similarly, we can predict the graph at 3 time steps ahead. We don’t have
a limit on
.
But generally, as we get further into the future, the predictions are
less accurate. This is with everything, not just graphs.
grpred3 <- predict_graph(graphlist[1:15], h = 3)
#> Warning: 2 errors (1 unique) encountered for arima
#> [2] missing value where TRUE/FALSE needed
#> Joining with `by = join_by(vertex)`
#> Joining with `by = join_by(From, To)`
#> Joining with `by = join_by(original)`
#> Joining with `by = join_by(original)`
grpred3
#> $graph_mean
#> IGRAPH 2fe3ee9 U--- 40 29 --
#> + edges from 2fe3ee9:
#> [1] 1-- 2 1-- 4 1-- 5 1-- 6 1--11 1--14 1--25 2-- 3 2-- 4 2--17
#> [11] 2--21 3-- 5 3-- 6 3-- 9 3--17 4-- 7 5--10 5--12 9--20 10--11
#> [21] 11--19 13--15 17--19 30--35 30--36 30--37 30--38 30--39 30--40
#>
#> $graph_lower
#> NULL
#>
#> $graph_upper
#> NULL
plot(grpred3$graph_mean)
ecount(grpred3$graph_mean)
#> [1] 29
vcount(grpred3$graph_mean)
#> [1] 40