---
title: "The R package netseer"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{The R package netseer}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
bibliography: bibliography.bib
---


```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  out.width = "100%"
)
```

# netseer <img src="man/figures/logo.png" align="right" height="138" />
<!-- badges: start -->
[![R-CMD-check](https://github.com/sevvandi/netseer/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/sevvandi/netseer/actions/workflows/R-CMD-check.yaml)
<!-- badges: end -->

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 [@kand2025graphpred]. We will illustrate an example in this vignette. 

## Installation

You can install the development version of netseer from [GitHub](https://github.com/) with:

``` r
# 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. 

```{r datagen}
library(netseer)
library(igraph)

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. 

### Plotting a couple of graphs

```{r plotdata}
plot(graphlist[[1]])

plot(graphlist[[8]])

plot(graphlist[[15]])
```

### Predicting the next graph 

Let's predict the next graph. The argument $h = 1$ specifies we want to predict the graph at the next time point. 
```{r grpred1}
grpred <- predict_graph(graphlist[1:15],h = 1)
grpred

plot(grpred$graph_mean)
ecount(grpred$graph_mean)
vcount(grpred$graph_mean)
```

### Predicting the graph at 2 time steps ahead

Now let us predict the graph at 2 time steps ahead with $h=2$.
```{r grpred2}
grpred2 <- predict_graph(graphlist[1:15], h = 2)
grpred2

plot(grpred2$graph_mean)
ecount(grpred2$graph_mean)
vcount(grpred2$graph_mean)
```
We see the predicted graph at $h=2$ has more vertices and edges than the graph at $h=1$.  

### 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 $h$. But generally, as we get further into the future, the predictions are less accurate. This is with everything, not just graphs. 

```{r grpred3}
grpred3 <- predict_graph(graphlist[1:15], h = 3)
grpred3

plot(grpred3$graph_mean)
ecount(grpred3$graph_mean)
vcount(grpred3$graph_mean)
```

# References