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 (Sevvandi Kandanaarachchi 2024). We will illustrate an example in this vignette.
You can install the development version of netseer from GitHub with:
# install.packages("devtools")
devtools::install_github("sevvandi/netseer")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_exp(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.
plot(graphlist[[1]])
plot(graphlist[[8]])
plot(graphlist[[15]])
###
Predicting the next graph
Let’s predict the next graph. The argument
grpred <- predict_graph(graphlist[1:15],h = 1)
#> Warning: 2 errors (1 unique) encountered for arima
#> [2] missing value where TRUE/FALSE needed
#> Registered S3 method overwritten by 'quantmod':
#> method from
#> as.zoo.data.frame zoo
grpred
#> $graph_mean
#> IGRAPH bb1ab36 U--- 35 23 --
#> + edges from bb1ab36:
#> [1] 17--19 11--19 10--11 9--20 5--12 5--10 3--17 3-- 9 3-- 6 2--21
#> [11] 2-- 4 2-- 3 1--25 1--14 1-- 6 1-- 5 1-- 4 1-- 2 7--31 10--32
#> [21] 20--33 14--34 20--35
#>
#> $graph_lower
#> NULL
#>
#> $graph_upper
#> NULL
plot(grpred$graph_mean)
ecount(grpred$graph_mean)
#> [1] 23
vcount(grpred$graph_mean)
#> [1] 35Now 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
grpred2
#> $graph_mean
#> IGRAPH 35d87d4 U--- 38 26 --
#> + edges from 35d87d4:
#> [1] 10--11 9--20 5--12 5--10 3--17 3-- 9 2--21 2-- 4 2-- 3 1--14
#> [11] 1-- 6 1-- 5 1-- 4 1-- 2 19--31 17--31 17--32 7--32 11--33 28--33
#> [21] 16--34 10--34 16--35 6--36 3--37 12--38
#>
#> $graph_lower
#> NULL
#>
#> $graph_upper
#> NULL
plot(grpred2$graph_mean)
ecount(grpred2$graph_mean)
#> [1] 26
vcount(grpred2$graph_mean)
#> [1] 38We see the predicted graph at
Similarly, we can predict the graph at 3 time steps ahead. We don’t have
a limit on
grpred3 <- predict_graph(graphlist[1:15], h = 3)
#> Warning: 2 errors (1 unique) encountered for arima
#> [2] missing value where TRUE/FALSE needed
grpred3
#> $graph_mean
#> IGRAPH 2ac93a1 U--- 41 29 --
#> + edges from 2ac93a1:
#> [1] 10--11 5--10 3--17 3-- 9 2-- 4 2-- 3 1--14 1-- 6 1-- 5 1-- 4
#> [11] 1-- 2 21--31 20--31 19--32 3--32 19--33 2--33 9--34 11--34 17--35
#> [21] 16--35 12--36 7--36 9--37 11--37 15--38 15--39 29--40 19--41
#>
#> $graph_lower
#> NULL
#>
#> $graph_upper
#> NULL
plot(grpred3$graph_mean)
ecount(grpred3$graph_mean)
#> [1] 29
vcount(grpred3$graph_mean)
#> [1] 41Sevvandi Kandanaarachchi, Stefan Westerlund, Ziqi Xu. 2024. “Predicting the Structure of Dynamic Graphs.” https://arxiv.org/abs/2401.04280.
