In mbojan/rgraph6: Representing Graphs as 'graph6', 'digraph6' or 'sparse6' Strings

library(rgraph6)
requireNamespace("igraph")
requireNamespace("knitr")

knitr::opts_chunk$set(
  collapse = TRUE
  # comment = "#>"
)

The formats

Let's generate an example directed and undirected graphs:

set.seed(123)
g_directed <- igraph::sample_gnm(12, 12, directed=TRUE)
g_undirected <- igraph::as.undirected(g_directed)
igraph::igraph_options(vertex.color="white", vertex.label.color="black",
                       edge.color="black", edge.arrow.size=0.5)
plot(g_directed)
plot(g_undirected)

Digraph6

The 'digraph6' is designed for directed graphs. Encoding g_directed will give:

as_digraph6(g_directed)

Graph6

The 'graph6' format is designed for undirected graphs. It is more efficient for dense graphs. Encoding g_undirected will give:

as_graph6(g_undirected)

Sparse6

The 'sparse6' format is designed for undirected graphs. It is more efficient for sparse graphs. Encoding g_undirected will give:

as_sparse6(g_undirected)

Main functions

Main functions for encoding network data are:

• graph_as_text()
• as_graph6()
• as_sparse6()
• as_digraph6()

Main functions for decoding are:

• adjacency_from_text()
• edgelist_from_text()
• igraph_from_text()
• network_from_text()

Implemented functions are shown on the following graph:

{ width="100%" }

Examples

Encode a list of 'igraph' objects

Generate a list of igraph objects:

set.seed(666)
igraph_list <- replicate(5, igraph::sample_gnp(10, 0.1, directed=FALSE), 
                         simplify = FALSE)

Encode as 'graph6' symbols:

as_graph6(igraph_list)

Encode as 'sparse6' symbols:

as_sparse6(igraph_list)

Decode a vector of different types of symbols

Using example data g6, d6, and s6 provided with the package:

# Create a vector with a mixture of 'graph6', 'digraph6' and 'sparse6' symbols
x <- c(g6[1], s6[2], d6[3])
x

# Parse to igraph objects (package igraph required)
igraph_from_text(x)

# Parse to network objects (package network required)
network_from_text(x)

Tidy graph databases

The formats shine if we need to store large number of graphs in a data frame. Let's generate a list of random graphs as igraph objects and store them in a data frame column of graph6 symbols:

# Generate list of igraph objects
set.seed(666)

d <- data.frame(
  g6 = as_graph6(replicate(
    10,
    igraph::sample_gnp(sample(3:12, 1), p=.5, directed=FALSE),
    simplify=FALSE
  ))
)
d

Nice and compact. We can go further by doing some computations and saving the results together with the graph data:

d2 <- within(
  d, {
    igraphs <- igraph_from_text(g6)
    vc <- vapply(igraphs, igraph::vcount, numeric(1))
    ec <- vapply(igraphs, igraph::ecount, numeric(1))
    density <- vapply(igraphs, igraph::edge_density, numeric(1))
})
d2$igraphs <- NULL
str(d2, 1)

... and even save it to a simple CSV file!

write.csv(d2, row.names = FALSE)

mbojan/rgraph6 documentation built on Feb. 4, 2024, 3:40 p.m.