Why we graph

Network visualisation is important and non-trivial. As Tufte (1983: 9) said:

“At their best, graphics are instruments for reasoning about quantitative information. Often the most effective way to describe, explore, and summarize a set of numbers – even a very large set – is to look at pictures of those numbers”

All of this is crucial with networks. As a first step, network visualisation – or graphing – offers us a way to vet our data for anything strange that might be going on, both revealing and informing our assumptions and intuitions.

It is also crucial to the further communication of the lessons that we have learned through investigation with others. While there may be many dead-ends and time-sinks to visualisation, it is worth taking the time to make sure that your main points are easy to appreciate.

Brandes et al (1999) argue that visualising networks demands thinking about:

• substance: a concise and precise delivery of insights to the researcher and/or readers
• design: the ergonomics of function are 98% of the purpose of good design, aesthetics only 2%
• and algorithm: the features of the e.g. the layout algorithm

Different approaches

There are several main packages for plotting in R, as well as several for plotting networks in R. Plotting in R is typically based around two main approaches:

• the ‘base’ approach in R by default, and
• the ‘grid’ approach made popular by the famous and very flexible {ggplot2} package.¹

Approaches to plotting graphs or networks in R can be similarly divided:

• two classic packages, {igraph} and {sna}, both build upon the base R graphics engine,
• newer packages {ggnetwork} and {ggraph} build upon a grid approach.²

While the coercion routines available in {manynet} make it easy to use any of these packages’ graphing capabilities, {manynet} itself builds upon the ggplot2/ggraph engine to help quickly and informatively graph networks.

Graphing

graphr(ison_lotr)

graphr(ison_lotr, node_color = "Race")

graphs(to_subgraphs(ison_lotr, "Race"),
       waves = c(1,2,3,4))

ison_lotr %>%
  mutate_ties(year = sample(1:12, 66, replace = TRUE)) %>%
  to_waves(attribute = "year", cumulative = TRUE) %>%
  grapht()

Titles and subtitles

Append ggtitle() to add a title. {manynet} works well with both {ggplot2} and {ggraph} functions that can be appended to create more tailored visualisations of the network.

graphr(ison_adolescents) + 
  ggtitle("Visualisation")

Arrangements

{manynet} also uses the {patchwork} package for arranging graphs together, e.g. side-by-side or above one another. The syntax is quite straight forward and is used throughout these vignettes.

graphr(ison_adolescents) + graphr(ison_algebra)
graphr(ison_adolescents) / graphr(ison_algebra)

Legends

While {manynet} attempts to provide legends where necessary, in some cases the legends offer insufficient detail, such as in the following figure, or are absent.

ison_lotr %>% 
  mutate(maxbet = node_is_max(node_betweenness(ison_lotr))) %>% 
  graphr(node_color = "maxbet")

{manynet} supports the {ggplot2} way of adding legends after the main plot has been constructed, using guides() to add in the legends, and labs() for giving those legends particular titles. Note that we can use "\n" within the legend title to make the title span multiple lines.

ison_lotr %>% 
  mutate(maxbet = node_is_max(node_betweenness(ison_lotr))) %>% 
  graphr(node_color = "maxbet") +
  guides(color = "legend") + 
  labs(color = "Maximum\nBetweenness")

An alternative to colors and legends is to use the ‘node_group’ argument to highlight groups in a network. This works best for quite clustered distributions of attributes.

graphr(ison_lotr, node_group = "Race")

Layouts

The aim of graph layouts is to position nodes in a (usually) two-dimensional space to maximise some analytic and aesthetically pleasing function. Quality measures might include:

• minimising the crossing number of edges/ties in the graph (planar graphs require no crossings)
• minimising the slope number of distinct edge slopes in the graph (where vertices are represented as points on a Euclidean plane)
• minimising the bend number in all edges in the graph (every graph has a right angle crossing (RAC) drawing with three bends per edge)
• minimising the total edge length
• minimising the maximum edge length
• minimising the edge length variance
• maximising the angular resolution or sharpest angle of edges meeting at a common vertex
• minimising the bounding box of the plot
• evening the aspect ratio of the plot
• displaying symmetry groups (subgraph automorphisms)

A range of graph layouts are available across the {igraph}, {graphlayouts}, and {manynet} packages that can be used together with graphr().

(graphr(ison_southern_women, layout = "kk") + ggtitle("Kamada-Kawai") |
   graphr(ison_southern_women, layout = "fr") + ggtitle("Fruchterman-Reingold") |
   graphr(ison_southern_women, layout = "stress") + ggtitle("Stress Minimisation"))

(graphr(ison_southern_women, layout = "bipartite") + ggtitle("Bipartite")) /
(graphr(ison_southern_women, layout = "hierarchy") + ggtitle("Hierarchy")) /
(graphr(ison_southern_women, layout = "railway") + ggtitle("Railway"))

graphr(ison_southern_women, layout = "alluvial") + ggtitle("Alluvial")

graphr(ison_southern_women, layout = "concentric") + ggtitle("Concentric")

graphr(ison_southern_women, layout = "eigen") + ggtitle("Eigenvector")

graphr(ison_southern_women, layout = "mds") + ggtitle("Multidimensional Scaling")
graphr(ison_southern_women, layout = "mds") + ggtitle("Multidimensional Scaling")

graphr(ison_southern_women, layout = "grid") + ggtitle("Grid")

Colors

graphr(ison_lotr,
           node_color = "Race")

graphr(ison_lotr,
           node_color = "Race") +
  ggplot2::scale_colour_hue()

graphr(ison_lotr,
           node_color = "Race") +
  ggplot2::scale_colour_grey()

graphr(ison_lotr,
           node_color = "Race") +
  ggplot2::scale_colour_manual(
    values = c("Dwarf" = "red",
               "Hobbit" = "orange",
               "Maiar" = "#DEC20B",
               "Human" = "lightblue",
               "Elf" = "lightgreen",
               "Ent" = "darkgreen")) +
  labs(color = "Color")

graphr(ison_lotr, node_color = "Race") + 
  scale_color_sdgs()

Further flexibility

For more flexibility with visualizations, {manynet} users are encouraged to use the excellent {ggraph} package. {ggraph} is built upon the venerable {ggplot2} package and works with tbl_graph and igraph objects. As with {ggplot2}, {ggraph} users are expected to build a particular plot from the ground up, adding explicit layers to visualise the nodes and edges.

library(ggraph)
ggraph(ison_greys, layout = "fr") + 
  geom_edge_link(edge_colour = "dark grey", 
                  arrow = arrow(angle = 45,
                                length = unit(2, "mm"),
                                type = "closed"),
                  end_cap = circle(3, "mm")) +
  geom_node_point(size = 2.5, shape = 19, colour = "blue") +
  geom_node_text(aes(label=name), family = "serif", size = 2.5) +
  scale_edge_width(range = c(0.3,1.5)) +
  theme_graph() +
  theme(legend.position = "none")

As we can see in the code above, we can specify various aspects of the plot to tailor it to our network.

First, we can alter the layout of the network using the layout = argument to create a clearer visualisation of the ties between nodes. This is especially important for larger networks, where nodes and ties are more easily obscured or misrepresented. In {ggraph}, the default layout is the “stress” layout. The “stress” layout is a safe choice because it is deterministic and fits well with almost any graph, but it is also a good idea to explore and try out other layouts on your data. More layouts can be found in the {graphlayouts} and {igraph} R packages. To use a layout from the {igraph} package, enter only the last part of the layout algorithm name (eg. layout = "mds" for “layout_with_mds”).

Second, using geom_node_point() which draws the nodes as geometric shapes (circles, squares, or triangles), we can specify the presentation of nodes in the network in terms of their shape (shape=, choose from 1 to 21), size (size=), or colour (colour=). We can also use aes() to match to node attributes. To add labels, use geom_node_text() or geom_node_label() (draws labels within a box). The font (family=), font size (size=), and colour (colour=) of the labels can be specified.

Third, we can also specify the presentation of edges in the network. To draw edges, we use geom_edge_link0() or geom_edge_link(). Using the latter function makes it possible to draw a straight line with a gradient. The following features can be tailored either globally or matched to specific edge attributes using aes():

• colour: edge_colour=

• width: edge_width=

• linetype: edge_linetype=

• opacity: edge_alpha=

For directed graphs, arrows can be drawn using the arrow= argument and the arrow() function from {ggplot2}. The angle, length, arrowhead type, and padding between the arrowhead and the node can also be specified.

To change the position of the legend, add the theme() function from {ggplot2}. The legend can be positioned at the top, bottom, left, or right, or removed using “none”.

For more see David Schoch’s excellent resources on this.

Exporting plots to PDF

We can print the plots we have made to PDF by point-and-click by selecting ‘Save as PDF…’ from under the ‘Export’ dropdown menu in the plots panel tab of RStudio.

If you want to do this programmatically, say because you want to record how you have saved it so that you can e.g. make some changes to the parameters at some point, this is also not too difficult.

After running the (gg-based) plot you want to save, use the command ggsave("my_filename.pdf") to save your plot as a PDF to your working directory. If you want to save it somewhere else, you will need to specify the file path (or change the working directory, but that might be more cumbersome). If you want to save it as a different filetype, replace .pdf with e.g. .png or .jpeg. See ?ggsave for more.