Visualising Networks

by James Hollway

Static preview: This is a static, read-only preview of the “Visualising Networks” tutorial, so the plots that the code produces and the exercise solutions are not shown here, and the quizzes are replaced with notes like this one. Install the package and run run_tute() at the R console to work through the tutorial interactively — running the code, seeing the graphs, and getting hints, solutions, and quizzes.

Why graph

Network visualisation is non-trivial; indeed it is very important, for at least two reasons.

First, visualisation is a crucial part of the process of data analysis. 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. The following image relates to the famous Anscombe’s quartet, which shows how different datasets can have identical statistical properties that are only revealed to be very different when graphed.

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. Drawing network graphs is key to exploring and understanding both the global structure of a network as well as smaller-scale structures such as nodal positions or communities within it.

Second, visualisation is a crucial part of communicating to others the lessons that we have learned through investigation. As Brandes et al (1999) argue, visualisation involves thinking about the substance of what you are trying to communicate, how to design it so that it is ergonomic and (ideally) aesthetic, and which algorithm is most appropriate to lay out the graph informatively. The aim is to offer a concise and precise delivery of insights.

There may be some dead-ends and time-sinks involved in visualising your data, but it is worth taking the time to explore your data and experiment with ways to make what you have learned over a longer period of time evident to others in a shorter period of time.

Catching up: This tutorial assumes you know what a network is, made up of nodes and ties , and that you can load or make network data in R. If any of that is hazy, work through the manynet tutorials first: run run_tute("Making") and run_tute("Manipulating") at the R console, or read their static versions on the manynet website.

New to network vocabulary?: Throughout this tutorial, key terms are italicised: hover over them for a definition, and a full glossary of the terms used appears at the end of the tutorial.

Aims

By the end of this tutorial, you should be able to:

•   Graph any compatible network with graphr() and read what the defaults show you
•   Map node and tie attributes to colour, shape, size, and groups
•   Set a consistent theme across all your plots, and tailor palettes for accessibility or print
•   Add titles, labels, and legends that help others read your graph
•   Choose an appropriate layout, and know when the distances between nodes can be interpreted
•   Arrange multiple graphs together with graphs() and animate change over time with grapht()
•   Plot centrality measures and other results with consistent plot() methods
•   Export publication-ready figures with ggsave()

Choose your own data: The worked examples below mostly use fict_lotr, a fictional network of affinities among Lord of the Rings characters bundled with manynet, plus a couple of classical datasets. But wherever there is an exercise box, you are encouraged to swap in a network that interests you. Remember the three flavours of bundled data as a rough difficulty ladder — Classic (ison_*, small & tidy), Fiction (fict_*, mid-sized & fun), Real-world (irps_*, larger & realistic) — and that you can browse the full list with table_data().

Getting started

On this page: Plotting approaches · Graphing approaches · Your first graph

Before we start, let’s load the packages used in this tutorial. autograph provides the graphing and plotting functions (and loads manynet, which provides the network data and manipulation verbs), netrics provides the network measures we will occasionally map onto graphs, and patchwork lets us arrange multiple plots together.

library(autograph)
library(netrics)
library(patchwork)

Plotting approaches

To understand graph and network visualisation with autograph, it is useful to review the different approaches already taken 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

In the case of base R graphics, plots are essentially written straight to the plotting device. This means that they are not easily modified after the fact: you would need to replot the whole thing to change something. Moreover, while there is an admirably clean aesthetic to base R graphics, it can be difficult to modify or extend them to your needs.

In the case of grid graphics, plots are built up in layers, and thus can be modified after the fact. That is, you can initialise a plot using ggplot2::ggplot(), specifying the data and mapping variables to various aesthetic features, and then add layers to it using + to add further points and lines, but also titles, legends, etc.

The following figure illustrates the difference between these two approaches.² Run the code to compare the two plots. (There are buttons to run the code you have entered, to start over, and — where available — to receive hints and solutions. You will use these throughout the tutorial.)

plot(mtcars$hp, mtcars$mpg,
     main = "Base R: MPG vs Horsepower",
     xlab = "Horsepower",
     ylab = "Miles per Gallon",
     pch = 19,
     col = "blue")
ggplot(mtcars, aes(x = hp, y = mpg)) +
  geom_point(color = "blue") +
  labs(title = "ggplot2: MPG vs Horsepower",
       x = "Horsepower",
       y = "Miles per Gallon")

Graphing approaches

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.³

Let’s see how the fict_lotr network would be plotted using igraph and ggraph, adding a title to each to facilitate comparison, but otherwise relying on default behaviour.

plot(as_igraph(fict_lotr),
     main = "igraph: fict_lotr")
ggraph::ggraph(as_tidygraph(fict_lotr)) +
  ggraph::geom_edge_link() +
  ggraph::geom_node_point() +
  ggtitle("ggraph: fict_lotr")

We can see here that igraph plots the network in a fairly basic way, straight to the plotting device (window). By default, it uses a force-directed layout (see the Layouts section below),⁴ colors the nodes orange, and prints node labels if they have them. However, the layout is not optimised for the size of the plotting window, the node labels are regularly overlapping, and the orange color with black borders is not particularly appealing or helpful for label legibility. It only works with ‘igraph’ objects.

In contrast, ggraph offers the trademark flexibility of the grammar of graphics approach. However, it requires the user to build up a plot from the ground up, which can be daunting for new users and fiddly even for experienced ones. Four lines are required to get even a basic plot, with an additional line required if a grey background is not desired. No labels or other information are added by default, and would also require additional lines. It works with ‘tidygraph’ objects, which are an additional layer on top of ‘igraph’ objects.

Your first graph

autograph builds upon these packages, but takes a somewhat different approach. It builds upon the ‘grid’ approach of ggplot2 and ggraph, lending itself to the additional layering and flexibility of those packages. Because it depends on the coercion routines available in manynet, it can be used with network-related objects from most common network analysis packages — igraph, network, tidygraph, matrices, edgelists, and more — without you needing to convert anything first. Unlike those packages, though, it offers concise and easy-to-use functions with sensible defaults for most common use cases, using the information that is available in the network object.

The first thing you will want to do when you import or create a new network dataset is draw it. Compared to the igraph and ggraph examples above, autograph::graphr() offers a much more concise way to draw the network. Try it now.

graphr(fict_lotr)

Note everything that happened without being asked: graphr() recognised that the network is labelled and printed the node labels, chose a deterministic layout (so you get the same picture every time), sized and spaced the labels to minimise overlap, and dropped the axes and grey background that mean nothing for networks. Because the network is undirected , there are no arrowheads; for a directed network, graphr() would draw them automatically.

Try it yourself: This section includes an interactive quiz in the live tutorial — run run_tute() at the R console to try it.

The package also offers methods for plotting statistics related to networks (e.g. degree distributions ) and models of them (e.g. goodness-of-fit plots). We will get to some of these later in this tutorial, and others are demonstrated in the tutorials of other {stocnet} packages. autograph also offers consistent theming across graphs and plots, so that you do not need to keep specifying the same options over and over again.

In the following pages, we’re going to go through a number of different ways of taking control of the graphing process. Click ‘Next Topic’ to continue.

In brief: graphr() graphs any manynet-compatible network object with sensible defaults inferred from the data: labels where the network is labelled, arrowheads where it is directed, a deterministic layout, and no chart junk. It returns a ggplot2 object, so anything you can do to a ggplot — adding layers, titles, scales with + — you can do to a graph.

Illustrating graphs

On this page: Shaping · Colouring · Sizing · Ties · Arrows · Taming · Free play

Once we have an initial graph of our network, we can start to explore features of the network and its structure in more detail. There are a number of different dimensions network researchers can play with to illustrate different aspects of the network. On her excellent and helpful website, Katya Ognyanova outlines some of these dimensions:

Nodes		Ties
Position	layout=	Arrows	automatic (directed ties)
Labels	labels=, node_group=	Type	automatic (signed ties)
Shape	node_shape=	Shape	automatic (reciprocated ties)
Size	node_size=	Size	edge_size=
Color	node_color=, node_colour=	Color	edge_color=, edge_colour=

The named arguments in the table above cover the aesthetics you will reach for most often. Several other visual features are not arguments at all: graphr() reads them off the data and sets them for you, so that a first graph already reads correctly without any tweaking. In particular:

• arrowheads are drawn (and trimmed back from the node) where the network is directed , and omitted where it is undirected ;
• ties curve apart slightly where a dyad is reciprocated , and are drawn straight otherwise;
• ties are drawn dashed where a signed network marks them negative, and solid where positive;
• self-ties (loops) are drawn where the network is complex ; and
• edges are drawn semi-transparent, so that denser bundles of ties read as darker.

You do not set these by hand — but because every graph is a ggplot2 object, you can always override them by dropping down to ggraph (see the Going further with ggraph section near the end of this tutorial). A handful of further arguments — node_group, label_repel/label_dist, edge_bundle, isolates, and snap — tune grouping, labelling, and how dense or disconnected networks are drawn; we meet each in its own section below.

Each of the mapping arguments can be given either a literal value (e.g. node_size = 6) or, more interestingly, the name of a node or tie attribute in the data (e.g. node_color = "Race"), in which case graphr() maps the attribute to that aesthetic and adds a legend where appropriate. Let’s go through some of these options in more detail.

Shaping nodes

One of the first things we might be interested in doing is understanding better the distribution of some categorical variable. Our fict_lotr dataset contains a variable called Race, so let’s try and change the shape of the nodes by this variable. Following the syntax shown in the table above, we just need to reference the variable name in the node_shape argument. Print the network first to check the attribute’s (case-sensitive) name, then graph it.

fict_lotr
graphr(fict_lotr, node_shape = "Race")

We can see here that there are six different races present.⁵ Unfortunately, this is a few too many different categories to be effectively distinguished by shape: at a glance, can you quickly find the triangles among the squares? Shape works best for two or three categories at most.

One place where shape excels, though, is distinguishing the node sets of a multimodal network — and there graphr() does it for you. For a two-mode network, nodes in the first mode are drawn as circles and nodes in the second mode as squares, with a “Mode” legend added automatically; were a third node set mapped to shape, it would be drawn as triangles. Graph the ison_southern_women network, where the women (first mode) appear as circles and the events they attended (second mode) as squares.

graphr(ison_southern_women)

Colouring nodes

Let’s try instead colouring the nodes by this “Race” variable. It is very similar to the shape example above. Can you complete the code yourself?

That’s much easier to read. Note how a legend has been added automatically, using the colours of whatever theme is currently set (more on themes soon).

How should we interpret this graph? Since the same colours seem to be clustered together, with the humans and hobbits each clustered together in the centre of the graph, and the elves clustered towards the left, we might infer that there is some homophily going on here — that characters tend to be connected to others of the same race — a hypothesis to test properly in another tutorial. Interpreting an attribute-coloured graph like this is often the first, informal step toward a more formal analysis.

An alternative to colouring the nodes is to use the node_group argument to highlight groups in a network. This puts a shaded area around nodes of the same group. For rather spatially clustered distributions, this can be a very effective way to show groupings, but it is sensitive to the layout used: if nodes of the same group are not close together, the shaded areas can overlap and make the graph harder to read.

graphr(fict_lotr, node_group = "Race")

Note that node_color and node_group can be used together, either to highlight different groupings, or to emphasise group assignment where the groups interpenetrate, as described above.

Sizing nodes

What about if we’re interested in a continuous variable instead of a categorical variable? While the fict_lotr dataset does not contain any continuous nodal variables, we can create one rather easily from the network itself. Let’s use each node’s degree , which is the number of ties incident/connecting to the node.

Beginner note: The |> symbol below is called a ‘pipe’. It passes the result of the expression on its left on to the function on its right, so the code below means “take fict_lotr, then add a Degree attribute to its nodes, then graph it with node size mapped to that attribute”. Piping or ‘chaining’ functions like this is very common in modern R, and we use it throughout these tutorials. mutate() and the other dplyr-style verbs for networks are covered in manynet’s “Manipulating Network Data” tutorial.

fict_lotr |>
  mutate(Degree = node_by_deg(fict_lotr)) |>
  graphr(node_size = "Degree")

Larger nodes are now the better-connected characters, and a size legend has been added. Who turns out to be the most connected character in the fellowship?

Try it yourself: This section includes an interactive quiz in the live tutorial — run run_tute() at the R console to try it.

Tying up loose ends

All this works similarly with ties/edges. Just replace node_ with edge_ in the arguments above, and you can control edges’ size and color. In the following example, we add two tie attributes: a continuous variable measuring how ‘close’ each tie is to others, and a binary variable indicating whether the tie is part of a triangle or not, and then colour the ties by the latter. Run the code, then try colouring or sizing the ties by "weight" instead.

fict_lotr |>
  mutate_ties(weight = tie_by_closeness(fict_lotr),
              is_tri = tie_is_triangular(fict_lotr)) |>
  graphr(edge_color = "is_tri")

Note also that some tie attributes are recognised automatically: if a network contains a tie attribute called weight, ties will be sized by weight without you asking, and a tie attribute called type will be used to distinguish tie types. Naming your attributes accordingly can save you some typing.

Pointing arrows

So far our example network has been undirected. For directed networks, graphr() adds arrowheads automatically, pointing from the sender to the receiver of each tie, and trims them back so they are not swallowed by the receiving node. Arrowheads are also scaled automatically with the width of the ties: thin ties get small arrowheads, thick ties get larger (but capped) ones, and ties of width zero lose their arrowheads entirely. This means arrowheads stay proportionate even when tie width is mapped from a weight attribute, as in the ison_networkers network of messages exchanged among early network researchers. You can also scale them manually: because the arrowheads follow the tie width, setting edge_size yourself resizes both together. Compare the automatic sizing with a manually thickened version.

(graphr(ison_networkers) + ggtitle("Automatic") |
   graphr(ison_networkers, edge_size = 1) + ggtitle("Manual (edge_size = 1)"))

Taming dense or disconnected networks

Two arguments help when a network is too dense, or too sparse, to read at a glance.

Larger, denser networks can turn into a ‘hairball’, where the sheer number of ties obscures everything. edge_bundle pulls ties that travel in similar directions into shared paths — like cabling them together — so that the main ‘highways’ of the network stand out. It is off by default; set edge_bundle = TRUE (or name a specific algorithm: "force", "path", or "minimal") to switch it on. Compare a dense random network with and without bundling.

rand <- manynet::generate_random(40, 0.1)
(graphr(rand) + ggtitle("Unbundled") |
   graphr(rand, edge_bundle = TRUE) + ggtitle("Bundled"))

At the other extreme, many networks contain isolates — unconnected nodes — which, under a force-directed layout, drift to the margins and squeeze the connected core into a clump. The isolates argument decides what happens to them: "legend" (the default) drops them from the drawing but records how many there were in the legend, "caption" notes them in a caption instead, and "keep" leaves them in place. Add two unconnected characters to fict_lotr and compare keeping them with noting them in the legend.

lotr_iso <- fict_lotr |>
  add_nodes(2, list(name = c("Tom Bombadil", "Goldberry")))
(graphr(lotr_iso, isolates = "keep") + ggtitle("keep") |
   graphr(lotr_iso, isolates = "legend") + ggtitle("legend"))

For very large real-world networks such as irps_blogs, the two work well together: edge_bundle = TRUE untangles the connected core while isolates = "legend" keeps its several hundred unconnected blogs from crowding that core out.

Free play

Your turn: choose another network and illustrate something about it. Pick a dataset with interesting node attributes — here is one suggestion per flavour:

Classic (small, easy)	Fiction (moderate)	Real-world (larger)
ison_lawfirm (various partner attributes)	fict_greys (Grey’s Anatomy: sex, race, sign)	irps_blogs (US political blogs: leaning)

Print the network first to see which attributes are available, then map one or two of them to colour, shape, size, or groups.

In brief: graphr() maps node and tie attributes to visual aesthetics by name: node_color, node_shape, node_size, and node_group for nodes, edge_color and edge_size for ties. Use colour or shape for categorical attributes (colour scales better), size for continuous ones, and node_group to shade spatially clustered memberships. For dense or disconnected networks, edge_bundle and isolates (see Taming dense or disconnected networks above) keep the picture legible.

Theming

On this page: Setting a theme · Hues · Grayscale · Manual override

Setting a theme

Perhaps you are preparing a presentation, representing your institution, department, or research centre at home or abroad. In this case, you may wish to theme the whole network with institutional colors and fonts. Indeed, you may even want to set a theme that is then reused across all your graphs and plots. autograph offers a number of themes that can be set using the stocnet_theme() function. Once set, the theme applies to every subsequent graph and plot in your session — no need to repeat yourself.

stocnet_theme("default")
graphr(fict_lotr, node_color = "Race")
stocnet_theme("iheid")
graphr(fict_lotr, node_color = "Race")
stocnet_theme("default")

Currently available themes include a number of institutional themes ("iheid", "ethz", "uzh", "rug", "unibe", "oxf", "unige", "cmu", "iast", "hwu") as well as stylistic ones ("default", "bw", "crisp", "neon", "rainbow"). Run stocnet_theme() without arguments to see which theme is currently set. More institutional scales and themes can be implemented upon pull request.

Who’s hue?

By default, graphr() will use a color palette that offers fairly good contrast and better accessibility. However, a different hue might offer a better aesthetic or identifiability for some nodes. Because the graphr() function is based on the grammar of graphics, it’s easy to extend or alter aesthetic aspects. Here let’s try and change the colors assigned to the different races in the fict_lotr dataset. Note that despite the argument being node_color, when overwriting the colors please use functions of the type ggplot2::scale_fill_*(), as it is the “fill” aesthetic that is being mapped to the variable in this case.

graphr(fict_lotr,
           node_color = "Race")

graphr(fict_lotr,
           node_color = "Race") +
  ggplot2::scale_fill_hue()

Try it yourself: This section includes an interactive quiz in the live tutorial — run run_tute() at the R console to try it.

At this stage, it is worth noting that not everyone experiences colours in the same way. Some people are colour-blind, whether by deuteranomaly, deuteranopia, protanomaly, or protanopia, and so it is worth checking that your visualisations are accessible to them.⁶ Others are less sensitive to colour distinctions. The old trope is that males are less sensitive to colour distinctions:⁷

Grayscale

Other times color may not be desired. Some publications require grayscale images. To use a grayscale color palette, replace _hue from above with _grey (note the ‘e’ spelling):

graphr(fict_lotr,
           node_color = "Race") +
  ggplot2::scale_fill_grey()

As you can see, grayscale is more effective for continuous variables or for very few discrete categories than for the six categories used here. If you need to distinguish several categories in print, consider combining grayscale with node_shape, or use the "bw" theme, which is designed for this purpose.

Manual override

Or we may want to choose particular colors for each category. This is pretty straightforward to do with ggplot2::scale_fill_manual(). Some common color names are available, but otherwise hex color codes can be used for more specific colors. Unspecified categories are coloured (dark) grey.

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

In brief: stocnet_theme() sets a theme once for all subsequent graphs and plots, with institutional and stylistic palettes included. Individual graphs can still be adjusted by appending ggplot2::scale_fill_*() functions — _hue() for a different palette, _grey() for print, _manual() for hand-picked colours — and it is worth checking your palette is colour-blind accessible.

Titles, labels, and legends

On this page: Labels · Titles · Legends

When it comes to communicating insights from network graphs to others, it is important to add in the contextual information that will help them understand what they are looking at. In this section, we will learn how to add titles, labels, and legends to graphs.

Labels

With our fict_lotr example above, because the network is itself labelled, graphr() automatically adds the node labels. If you do not want these labels, you can remove them from the network before passing it on to graphr(), or more simply use the argument labels = FALSE.

graphr(fict_lotr, labels = FALSE)

Without the labels, the structure of the network is clearer and easier to interpret, though we lose the information about which node is which character. Which you prefer depends on what the graph is for: exploring who-is-who, or communicating overall structure.

Going further: By default graphr() repels labels away from each other and from nodes so that they do not overlap. Two further arguments offer finer control: label_repel = FALSE places labels at a fixed offset instead, and label_dist controls how far labels sit from their nodes (in points). For crowded graphs, also consider labelling only some nodes, e.g. mutate(name = ifelse(node_is_max(node_by_deg(.)), name, "")).

Titles

autograph works well with both ggplot2 and ggraph functions that can be appended to create more tailored visualisations. Let’s try this by adding a title to a plot. Append (with a +) labs(title = ) to add a title to a plot, say “My graph”, and then add also a subtitle (an argument to that function), say “I did this”.

Note that you can also use ggtitle() to do the same thing, but if you just remember labs() you can also use it to add labels for x and y axes, and legends (see below).

Legends

While autograph attempts to provide legends where necessary, in some cases the legends offer insufficient detail, or are absent, such as in the following figure, where we highlight the node with the highest betweenness centrality.

fict_lotr |>
  mutate(maxbet = node_is_max(node_by_betweenness(fict_lotr))) |>
  graphr(node_color = "maxbet")

Which node is highlighted here, and why might that be? Without a legend title, a reader cannot know what the colour signifies. autograph 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.

fict_lotr |>
  mutate(maxbet = node_is_max(node_by_betweenness(fict_lotr))) |>
  graphr(node_color = "maxbet") +
  guides(color = "legend") +
  labs(color = "Maximum\nBetweenness")

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”.

In brief: labs() adds titles, subtitles, and legend titles; guides() forces or removes legends; labels = FALSE hides node labels, and label_repel/label_dist fine-tune their placement. A graph that leaves your hands should be readable without you standing next to it explaining.

Layouts

On this page: Force-directed · Layered · Circular · Spectral · Grid · Manual

The aim of graph layouts is to position nodes in a (usually) two-dimensional space to maximise some analytic and aesthetically pleasing function. Unlike the maps and scatterplots you may be used to, where a node is drawn on a network graph is usually not data: it is chosen by an algorithm to make the structure readable. Knowing which algorithm — and what can and cannot be read off the result — is the point of this section. Quality measures a layout algorithm might attend to 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)

Graph layouts available in the igraph, ggraph, graphlayouts, and autograph packages can be used in graphr(). These can be specified using the layout argument. For these examples we will use ison_southern_women, a classical two-mode network of women attending events, because two-mode networks make the differences between layouts especially visible. In the following sections, we review some of the most common types of layouts.

Force-directed layouts

Force-directed layouts update some initial placement of vertices through the operation of some system of metaphorically-physical forces. These might include attractive and repulsive forces.

(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"))

The Kamada-Kawai (KK) method inserts a spring between all pairs of vertices that is the length of the graph distance between them. This means that edges with a large weight will be longer. KK offers a good layout for lattice -like networks, because it will try to space the network out evenly.

The Fruchterman-Reingold (FR) method uses an attractive force between directly connected vertices, and a repulsive force between all vertex pairs. The attractive force is proportional to the edge’s weight, thus edges with a large weight will be shorter. FR offers a good baseline for most types of networks.

The Stress Minimisation (stress) method is related to the KK algorithm, but offers better runtime, quality, and stability and so is generally preferred. Indeed, autograph uses it as the default for most networks. It has the advantage of returning the same layout each time it is run on the same network.

Try it yourself: This section includes an interactive quiz in the live tutorial — run run_tute() at the R console to try it.

Other force-directed layouts available include:

• Simulated annealing (Davidson and Harel 1993): "dh"
• Graph embedder (Frick et al. 1995): "gem"
• Graphopt (Schmuhl): "graphopt"
• Distributed recursive graph layout (Martin et al. 2008): "drl"

Layered layouts

Layered layouts arrange nodes into horizontal (or vertical) layers, positioning them so that they reduce crossings. These layouts are best suited for directed acyclic graphs, two-mode networks, or other data with a natural hierarchy or ordering.

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

Note that "hierarchy" and "railway" use a different algorithm to igraph’s "bipartite", and generally perform better, especially where there are multiple layers. Whereas "hierarchy" tries to position nodes to minimise overlaps, "railway" sequences the nodes in each layer to a grid so that nodes are matched as far as possible. For the "hierarchy" layout you can also steer which set sits where by passing a center argument — "events" or "actors" for a two-mode network, or the name of a particular node — which helps when the default places the less interesting set on top.

graphr(ison_southern_women, layout = "hierarchy", center = "events")

If you want to flip the horizontal and vertical, you could flip the coordinates, or use something like the following layout.

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

Other layered layouts include:

• Tree: "tree"
• Dominance layouts

Circular layouts

Circular layouts arrange nodes around (potentially concentric) circles, such that crossings are minimised and adjacent nodes are located close together. In some cases, location or layer can be specified by attribute or mode.

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

The "concentric" layout can also place nodes on rings by a grouping you choose: pass a membership argument (a node attribute name, or a vector the same length as the number of nodes). Ring the Lord of the Rings characters by their race.

graphr(fict_lotr, layout = "concentric", membership = "Race")

Other such layouts include:

• circular: "circle"
• sphere: "sphere"
• star: "star"
• arc or linear layouts: "linear"

Spectral layouts

Spectral layouts arrange nodes according to the eigenvalues of the Laplacian matrix of a graph. These layouts tend to exaggerate the clustering of like-nodes and the separation of less similar nodes in two-dimensional space.

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

Somewhat similar are multidimensional scaling (MDS) techniques, which visualise the similarity between nodes in terms of their proximity in a two-dimensional (or more) space.

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

Other such layouts include:

• Pivot multidimensional scaling: "pmds"

Try it yourself: This section includes an interactive quiz in the live tutorial — run run_tute() at the R console to try it.

Grid layouts

Grid layouts arrange nodes based on some Cartesian coordinates. These can be useful for making sure all nodes’ labels are visible, but horizontal and vertical lines can overlap, making it difficult to distinguish whether some nodes are tied or not.

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

Other grid layouts include:

• orthogonal layouts for e.g. printed circuit boards
• grid snapping for other layouts

That last point deserves a demonstration. Rather than committing to a full grid, graphr()’s snap = TRUE argument keeps whatever layout you asked for but snaps its coordinates onto a grid — trading a little positional accuracy for the label legibility of a grid. Compare the stress layout with its snapped version.

(graphr(fict_lotr) + ggtitle("stress") |
   graphr(fict_lotr, snap = TRUE) + ggtitle("stress + snap"))

Manual layouts

Whatever their differences, all these layout algorithms do the same job: they return a table of node coordinates. Nothing stops you computing that table yourself, inspecting it, adjusting a coordinate or two, and handing the result back to graphr() via its x and y arguments. This is handy when a layout is almost right — say one label sits awkwardly, or you want a particular node set apart — or when you need the same hand-tuned positions across several figures. Compute a stress layout for fict_lotr, inspect the coordinate table, banish Gollum to the top-right corner, and re-graph.

lo <- ggraph::create_layout(as_tidygraph(fict_lotr), layout = "stress")
head(lo[, c("name", "x", "y")])
lo$x[lo$name == "Gollum"] <- max(lo$x) + 1
lo$y[lo$name == "Gollum"] <- max(lo$y) + 1
graphr(fict_lotr, x = lo$x, y = lo$y)

The same trick lets you reuse a layout across plots (compute once, pass the same x/y to each call), which keeps node positions identical between figures — useful when readers need to compare them.

Going further: autograph also provides its own special-purpose layouts — "configuration", "lineage", "multilevel", "triad"/"quad", and layouts that align nodes by partition — documented at ?layout_partition and friends. Several layouts take a layout-specific extra argument (passed through ...) to control how nodes are ordered: "concentric" a membership, "multilevel" a level, and "lineage" a rank — each a node attribute name or a vector. See ?graphr for the full list.

In brief: Pass layout = to graphr() to choose among force-directed ("stress", "fr", "kk"), layered ("hierarchy", "railway", "alluvial"), circular ("concentric", "circle"), spectral ("eigen", "mds"), and grid layouts. Force-directed layouts are illustrative — do not over-interpret distances; spectral/MDS layouts place nodes by measured similarity; layered layouts suit two-mode or hierarchical data. And since every layout is just a table of coordinates, you can always compute one with ggraph::create_layout(), adjust it, and pass it back via graphr()’s x and y arguments.

Multiple graphs

On this page: Arrangements · Sets · Dynamics

Sometimes one graph is not enough: we want to compare two networks, several subgraphs, or the same network at different points in time.

Arrangements

autograph uses the patchwork package for arranging graphs together, e.g. side-by-side or above one another. The syntax is quite straightforward and is used throughout these vignettes/tutorials. Basically, you just use + or | to put graphs side-by-side, and / to put them above one another. Parentheses can be used to group graphs together. Try graphing fict_lotr and ison_algebra side-by-side, and then one above the other.

Sets

graphr() is not the only graphing function included in autograph. To graph sets of networks together, graphs() makes sure that two or more networks are plotted together, using a consistent layout and theme across the panels so that they can be compared. This might be a set of ego networks, subgraphs , or waves of a longitudinal network.

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

What is happening here is that to_subgraphs() is creating a list of subgraphs — one per race — and then graphs() is plotting them together at once with the same set of aesthetic parameters. The waves argument selects which networks in the list to plot — here the first four of the six race subgraphs. Left to its own devices, graphs() plots just the first and last networks of longer lists, which suits before-and-after comparisons of longitudinal networks.

When the panels share the same nodes, graphs() computes a single layout and reuses it across panels so that positions line up and can be compared; by default it uses the "first" network’s layout, but based_on = "last" or "both" are available. Sharing a layout means every panel has to draw every node, so in that case isolates are kept in place.

Dynamics

grapht() is another alternative to graphr(), this time rendering network changes over time as an animated gif. Longitudinal networks (with discrete waves) and dynamic networks (with dated changes) are both supported. Nodes appear, move, and fade as they enter and exit the network, and node positions transition smoothly between waves. Run the following to animate a randomly-evolving version of our Lord of the Rings network. (Be patient — rendering an animation takes considerably longer than drawing a static graph, and requires the suggested gganimate and gifski packages.)

fict_lotr |>
  mutate_ties(wave = sample(2001:2012, manynet::net_ties(fict_lotr), replace = TRUE)) |>
  to_waves(cumulative = TRUE) |>
  grapht()

Note that here, as with weight and type in the previous section, attribute naming matters a little: a time attribute called wave marks the network as longitudinal for manynet, so to_waves() (and grapht() itself, passed such a network directly) will split it without being told which attribute to use. From manynet 2.2.2, any other name (say, year) works just as well — it only needs declaring via to_waves()’s attribute argument.

Going further: Animation constrains a few things that a static graph allows. grapht()’s isolates argument takes "keep" (the default) or "fade" (fading nodes out in the waves where they have no ties), rather than graphr()’s "legend"/"caption". And because they do not translate cleanly from frame to frame, node_group hulls, edge_bundle, the slight curve on reciprocated ties, and self-loops are not drawn in animations. Labels, too, are placed at a fixed offset rather than repelled, and are hidden by default once a network has more than 30 nodes (pass labels = TRUE to force them).

In brief: Combine individual graphs with patchwork operators (+/| beside, / above), graph lists of related networks with graphs() for comparable panels, and animate longitudinal or dynamic networks with grapht().

Going further with ggraph

For more flexibility with visualisations, autograph 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. This means more typing, but near-total control.

library(ggraph)
ggraph(fict_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.

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

In brief: Because graphr() returns a ggplot object, you can go a long way just appending ggplot2/ggraph layers to it. When you need full control over every geom, build the plot directly in ggraph — the skills transfer directly, since autograph uses ggraph underneath.

Plotting results

While researchers will probably want to start with using graphr() to visualise the network itself, autograph also offers plot() methods for a number of different network-related objects, so that plot(result) “just works” without you needing to remember a special function for each object. These include measures of centrality, cohesion, and clustering, as well as goodness-of-fit plots for network models from packages such as RSiena, ergm, and {MoNAn}. Usefully, all these plots use the same theming system as graphr(), so that you can set a theme once and have it apply to all your graphs and plots. Let’s try this now with a few examples, plotting the distributions of two centrality measures under two themes.

stocnet_theme("default")
plot(node_by_degree(fict_lotr)) +
plot(node_by_closeness(fict_lotr))
stocnet_theme("oxf")
plot(node_by_degree(fict_lotr)) +
plot(node_by_closeness(fict_lotr))
stocnet_theme("default")

Each plot shows the distribution of a node measure across the network — here how unequal the characters’ degree and closeness centralities are. This is a very simple example, but the same principle applies to all plots in autograph: one can set a theme once and have it apply to all plots, and one can always add additional ggplot2 layers to any plot to further customise it — titles and labels, but also trend lines, confidence intervals, and so on. The plot methods for model results are demonstrated in the tutorials of the packages that produce those results.

Exporting plots

We can save the plots we have made 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 ggsave() to save it to disk:

graphr(fict_lotr, node_color = "Race")
ggsave("lotr_race.pdf")
ggsave("lotr_race.png", width = 9, height = 6, dpi = 300)

ggsave() infers the file type from the extension (.pdf, .png, .jpeg, .svg, …), saves to your working directory unless you specify a path, and lets you fix the exact width, height, and resolution (dpi) your publisher requires. For print, prefer vector formats (.pdf, .svg), which stay sharp at any size; see ?ggsave for more.

Animations made with grapht() are saved slightly differently: use gganimate::anim_save("my_animation.gif"), which works just like ggsave() but for the last animation rendered.

Summary

Well done — you have completed the tutorial on visualising networks! Along the way, you have learned to use these functions:

Function	What it does
graphr()	graphs any manynet-compatible network with sensible defaults
graphr(..., node_color/node_shape/node_size/node_group)	maps node attributes to aesthetics
graphr(..., edge_color/edge_size)	maps tie attributes to aesthetics
graphr(..., labels, label_repel, label_dist)	controls node labelling
graphr(..., layout, snap)	chooses and adjusts the layout algorithm
graphr(..., x, y)	places nodes at manually supplied coordinates
ggraph::create_layout()	returns a layout’s table of node coordinates for tweaking
graphr(..., edge_bundle, isolates)	tames large, dense, or disconnected networks
stocnet_theme()	sets a consistent theme for all graphs and plots
ggplot2::scale_fill_hue(), _grey(), _manual()	overrides node colour palettes
labs(), ggtitle(), guides()	adds titles, axis and legend labels
graphs()	graphs a list of networks as comparable panels
grapht()	animates a longitudinal or dynamic network as a gif
plot()	plots measures, motifs, and model results consistently
ggsave()	exports the last plot at publication quality

When you are ready, continue with the tutorials in the other {stocnet} packages — on network structure and centrality in netrics, and on diffusion and regression in migraph — where the measures you can now visualise are properly introduced. Run run_tute() at the console to see all available tutorials.

Glossary

Here are some of the terms that we have covered in this tutorial:

Betweenness

The betweenness centrality of a node is the proportion of shortest paths between all pairs of nodes that pass through that node.

Closeness

The closeness centrality of a node is the reciprocal of the sum of its distances to all other nodes.

Community

A community is a set of nodes more densely connected to one another than to other nodes in the network.

Complex

A complex network is one that includes or can include loops or self-ties.

Degree

The degree of a node is the number of connections it has.

Directed

A directed network is a network where the ties have a direction, from a sender to a receiver.

Distribution

A degree distribution is the frequency distribution of the degrees of the nodes in a network.

Homophily

A tendency for nodes to connect to similar nodes.

Isolate

An isolate is a node with degree equal to zero.

Label

A labelled network includes unique labels for each node (or ties) in the network.

Lattice

A network that can be drawn as a regular tiling.

Longitudinal

A longitudinal network is one observed in two or more discrete waves or panels over time.

Network

A network comprises one or more sets of nodes, one or more sets of ties among them, and potentially some node, tie, or network-level attributes.

Node

A node or vertex is an entity or actor within a network.

Reciprocity

A measure of how often nodes in a directed network are mutually linked.

Signed

A signed network is one where ties are marked as positive or negative, such as friendship and enmity or alliance and conflict.

Subgraph

A subgraph comprises a subset of the nodes and ties in a network.

Tie

A tie, edge, or link is a connection or relationship between two nodes.

Triangle

A cycle of length three in a network.

Twomode

A two-mode (or bipartite) network is a network with two different sets of nodes, where ties connect only nodes from different sets, such as people and the events they attend.

Undirected

An undirected or line network is one in which tie direction is undefined.

Weighted

A weighted network is where the ties have been assigned weights.