Package 'netseer'

Generates a bigger graph using exponential growth.

Description

Generates a bigger graph using parameters for node and edge growth. If a sequence of graphs are created, the number of nodes in this sequence would exponentially increase.

Usage

generate_graph_exp(
  gr = NULL,
  del_edge = 0.1,
  new_nodes = 0.1,
  edge_increase = 0.1
)

Arguments

gr	The input graph to generate the next graph. If set to NULL a graph using igraph::sample_pa is used as the input graph.
del_edge	The proportion of edges deleted from the input graph. Default set to 0.1.
new_nodes	The proportion of nodes added to the input graph. Default set to 0.1.
edge_increase	The proportion of edges added to the input graph. Default set to 0.1.

Value

A graph.

Examples

set.seed(1)
gr <- generate_graph_exp()
gr

---

Generates a bigger graph by linear growth.

Description

Generates a bigger graph using parameters for node and edge growth. If a sequence of graphs are created, the number of nodes would linearly increase.

Usage

generate_graph_linear(
  gr = NULL,
  del_edge = 1,
  new_nodes = 1,
  edge_increase = 1,
  edges_per_new_node = 3
)

Arguments

gr	The input graph to generate the next graph. If set to NULL a graph using igraph::sample_pa is used as the input graph.
del_edge	The number of edges deleted from the input graph. Default set to 1.
new_nodes	The number of nodes added to the input graph. Default set to 1.
edge_increase	The number of edges added to the input graph. Default set to 1.
edges_per_new_node	The number of edges added to the new nodes. Default set to 3.

Value

A graph.

Examples

set.seed(1)
gr <- generate_graph_linear()
gr

---

Loads graphs to memory using a desired method.

Description

This function loads graphs from the file system into the R environment. There are two loading options: Loading all files from a directory. Loading individual graphs.

Usage

load_graphs(use_directory = NULL, use_list = NULL, format)

Arguments

use_directory	The absolute path to a directory that contains graph files to load.
use_list	A list of absolute paths to individual graph files to load.
format	Formats supported by igraph::read_graph.

Value

A list of graphs in igraph format.

Examples

## Not run: 
graph_dir_list <- list()
path1 <- normalizePath("graph1.gml")
path2 <- normalizePath("graph2.gml")
graph_dir <- append(graph_dir_list, path1 )
graph_dir <- append(graph_dir_list, path2 )

grlist <- read_graph_list(graph_dir_list, "gml")
grlist

## End(Not run)

---

Gives an error measurement for predicted graphs

Description

This function compares the predicted graph with the actual and comptues the node and edge error as a proportion

Usage

measure_error(actual, predicted)

Arguments

actual	The ground truth or actual graph.
predicted	The predicted graph.

Value

The node error and edge error as a proportion.

Examples

data(syngraphs)
# Taking the 20th graph as the actual and the 19th graph as predicted.
measure_error(syngraphs[[20]], syngraphs[[19]])

---

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 = 8,
  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 6 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 uses proportional weights according to the history. Option 5 uses proportional weights, but as the network is more in the past, it gives less weight. Option 5 uses linearly decaying proportional weights. Option 6 uses harmonically decaying weights. That is the network at T is given weight 1, T-1 is given weight 1/2 and so on. Option 7 uses 1 for edges that are present in the last graph. Option 8 is a slightly different to Option 7. It uses 1 for edges in the last seen graph and the weights_param for new edges. Default is set to 8.
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

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
}
grpred <- predict_graph(graphlist[1:15], conf_level2 = 90, weights_opt = 6)
grpred

---

Saves either a single graph or list of graphs to disk.

Description

This function saves a single graph or list of graphs to a specified location on the file system in a specified format.

Usage

save_graphs(graph, file_path, filetype = ".gml", format)

Arguments

graph	Either a single igraph graph, or a list of igraph graphs.
file_path	The Absolute path to save the graph/s to.
filetype	The filetype extension to append to the graph file name, e.g. ".gml"
format	Formats supported by igraph::read_graph.

Value

A list of graphs in igraph format.

Examples

## Not run: 
library(igraph)
sample_graph  <- igraph::graph_from_literal(A-B, B-C)
path <- "/path/to/save/to/"
save_graphs(sample_graph, path, ".gml", "gml")

## End(Not run)

---

A dataset containing synthetic graphs

Description

This dataset contains a list of synthetic igraph objects

Usage

syngraphs

Format

A list of 20 igraph graphs

Examples

data(syngraphs)
syngraphs[[1]]

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