predict_graph {netseer}

Predicts a graph from a time series of graphs.

Description

This function predicts the graph at a future time step using a time series of graphs.

Usage

predict_graph(
  graphlist,
  formulation = 2,
  conf_level1 = NULL,
  conf_level2 = 90,
  dense_opt = 2,
  weights_opt = 4,
  weights_param = 0.001,
  h = 1
)

Arguments

graphlist

A list of graphs in igraph format.

formulation

Formulation 2 includes an additional condition constraining total edges by the predicted value. Formulation 1 does not have that constraint. Formulation 2 gives more realistic graphs due to that constraint. Default is set to 2.

conf_level1

A value between 50 and 100 denoting the confidence interval for the number of predicted nodes in the graph. If set to NULL the predicted graph has the mean number of predicted nodes. If set to 80 for example, there would be 3 predicted graphs. One with mean number of predicted nodes, and the other two with the number of nodes corresponding to lower and upper confidence bounds.

conf_level2

The upper confidence bound for the degree distribution. Default set to 90.

dense_opt

If set to 2 the dense option in R package lpSolve will be used.

weights_opt

Weights option ranging from 1 to 4 used for different edge weight schemes. Weights option 1 uses uniform weights for all edges. Option 2 uses binary weights. If the edge existed in a past graph, then weight is set to 1. Else set to 0. All possible new edges are assigned weight 1. Option 3 is a more selective version. Option 4 is proportional weights according to the history.

weights_param

The weight given for possible edges from new vertices. Default set to 0.001.

h

The prediction time step. Default is h = 1.

Value

A list of predicted graphs. If conf_level1 is not NULL, then 3 graphs are returned one with the mean number of predicted nodes and the other 2 with the number of nodes equal to the lower and upper bound values of prediction. If If conf_level1 is NULL, only the mean predicted graph is returned.

Examples

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(gr,
                        del_edge = del_edge_val,
                        new_nodes = new_nodes_val,
                        edge_increase = edge_increase_val )
  graphlist[[i]] <- gr
}
grpred <- predict_graph(graphlist[1:15], conf_level2 = 90, weights_opt = 4)
grpred