Visualising Networks
Author: by James Hollway

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## 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")

1. Perhaps of interest, `gg` stands for the Grammar of Graphics (https://doi.org/10.1007/0-387-28695-0).↩︎

2. For more on the differences between base and grid graphics, see https://flowingdata.com/2016/03/22/comparing-ggplot2-and-r-base-graphics/.↩︎

### 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 colours 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 colour 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.

3. Others include: ‘Networkly’ for creating 2-D and 3-D interactive networks that can be rendered with plotly and can be easily integrated into shiny apps or markdown documents; ‘visNetwork’ interacts with javascript (vis.js) to make interactive networks (http://datastorm-open.github.io/visNetwork/); and ‘networkD3’ interacts with javascript (D3) to make interactive networks (https://www.r-bloggers.com/2016/10/network-visualization-part-6-d3-and-r-networkd3/).↩︎

4. Which incidentally returns a different layout each time it is run.↩︎

### 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 node labels — but only for the most central characters, since 36 labels at once would hide the network behind them (the Labels section below shows how to choose differently), 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.

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 (and, where it is large, only for the nodes that stand out), 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=`, `isolates=`, `snap=` | Arrows | automatic (directed ties)
Labels | `labels=`, `label_repel=`, `label_dist=`, `node_group=` | Type | automatic (signed ties)
Shape | `node_shape=` | Shape (curve) | automatic (reciprocated ties), `edge_bundle=`
Size | `node_size=` | Size (width) | `edge_size=`
Colour | `node_colour=`/`node_color=` | Colour | `edge_colour=`/`edge_color=`

Beginner note: As the table shows, both spellings work: `node_colour=` and `node_color=` are the same argument, as are `edge_colour=` and `edge_color=`, and the same goes for `{ggplot2}`‘s `colour`/`color` aesthetics and `scale_colour_*()`/`scale_color_*()` functions. This tutorial is written in British English and so says ’colour’ throughout, but you should use whichever spelling comes naturally to you.

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). Further arguments 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_colour = "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)

5. Though the keen-eyed and well-read among you will have noticed that there are some racial assignments that are debatable.↩︎

### 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?

    # Use the same syntax as with node_shape, but with the node_colour argument.
    # Remember to name the attribute in quotation marks.

    graphr(fict_lotr, node_colour = "Race")

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_colour` 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?

### 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 colour. 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_colour = "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

Sometimes networks are just a dense hairball. This is a technical term to describe networks with many high-degree nodes and many ties, where the sheer number of ties obscures the structure of the network. Autograph includes three arguments that can help with this.

#### Bundling ties

The first option is to draw all of the ties ‘bundled’ together, which can reveal where the most common paths through the network are. `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. `ison_lawfirm` records 71 lawyers and 2571 ties between them, which is about as thick a hairball as a network this small can be. Compare it drawn with and without bundling (turn backbone off too for clearest comparison results).

    graphr(ison_lawfirm, backbone = FALSE) + ggtitle("Unbundled") |
      graphr(ison_lawfirm, backbone = FALSE, edge_bundle = "path") + ggtitle("Bundled")

I find this works best with networks that are at least moderately dense, and sometimes requires a little bit of playing around to get a good result.

#### Backbones

By contrast, backbone changes which ties the picture is built around. Ties that carry more weight/structure than expected by a null model local to their endpoints are in essence what the network would be if it were stripped back to its skeleton. `graphr()` then draws the layout according to this skeleton and fades other ties into the background to further emphasise the main structure.

Most of the time you will not have to ask for this. Networks of 50+ nodes with 8 ties each on average is drawn this way by default. But you can specify `backbone = FALSE` to turns it off, `backbone = TRUE` to force it on, or you can name a filter — `"disparity"`, `"lans"`, `"noise"`, `"mlf"`, or `"simmelian"` — or threshold. Compare `ison_lawfirm` drawn with and without its backbone.

    (graphr(ison_lawfirm, node_colour = "office", backbone = FALSE) +
       ggtitle("Every tie alike") |
       graphr(ison_lawfirm, node_colour = "office", backbone = TRUE) +
       ggtitle("Backbone"))

The offices are hardly visible on the left. On the right they separate, because the ties that hold each office together are the ties the filter keeps.

Only the layouts that read tie lengths are laid out this way: `"stress"` (the default), `"fr"`, `"drl"` and `"kk"`. Every other layout, including those whose coordinates already mean something such as `"layered"` or `"scaling"`, keeps its coordinates and only fades its ties. Signed networks have no backbone, since these null models have no place for a negative weight, and are drawn as they were.

Bundling and backbones answer the same problem from different ends, so try one before reaching for both. A bundled tie cannot carry a fading of its own — bundling merges ties into shared paths — so where both are asked for, the backbone still shapes the layout but every tie is drawn alike.

#### Isolates

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`, these work well together: a backbone picks out the ties that hold the connected core together (or `edge_bundle = TRUE`, if you would rather see the paths the ties take than which of them matter most), 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_colour`, `node_shape`, `node_size`, and `node_group` for nodes, `edge_colour` 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`, `backbone` and `isolates` (see Taming dense or disconnected networks above) keep the picture legible.

## Theming

On this page: Setting a theme · Hues · Colour blindness · Greyscale · Manual override · Medium

### 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 colours 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_colour = "Race")
    stocnet_theme("iheid")
    graphr(fict_lotr, node_colour = "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"`, `"clay"`, `"rainbow"`). Run `stocnet_theme()` without arguments to see which theme is currently set. More institutional scales and themes can be implemented upon pull request.

A theme lasts for the session in which you set it, and a new session starts on the default again. Where a theme is your usual one, `persist = TRUE` remembers it, by writing the name to your user configuration directory:

    # stocnet_theme("iheid", persist = TRUE)   # remembered next session too
    stocnet_theme()

Nothing is written to disk unless you ask for it. Setting any theme with `persist = FALSE`, the default, forgets a choice you persisted earlier, so `stocnet_theme("default", persist = FALSE)` puts you back where you began.

A theme sets a typeface as well as a palette, but only where that typeface is installed and R can see it. `list_fonts()` lists the families R can see, and `ag_font()` reports the one the current theme settled on.

    ag_font()
    head(list_fonts("sans"))

If `ag_font()` returns `"sans"`, the theme found none of the fonts it prefers, and your graphs will look more generic than they should. Install the missing family — many are free from Google Fonts — then install the `{systemfonts}` package so that R can see the fonts on your system, and set the theme again. `?stocnet_theme` sets out the steps for each operating system.

### Who’s hue?

By default, `graphr()` will use a colour 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 colours assigned to the different races in the `fict_lotr` dataset. Note that despite the argument being `node_colour`, when overwriting the colours 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_colour = "Race")

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

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

6. The viridis and colorspace packages have excellent vignettes on this.↩︎

7. Though see https://blog.xkcd.com/2010/05/03/color-survey-results/ for a more nuanced take.↩︎

### Seeing what others see

About one man in twelve, and one woman in two hundred, sees colour differently from the palette designer. The most common form, deuteranopia, confuses reds with greens — which is precisely the pairing a “stop/go” palette relies on.

`{autograph}` gives you two functions for checking this. `simulate_colorblind()` shows you a set of colours as such a viewer sees them, and `check_separation()` scores how far apart colours are, taking the worst case across normal vision and each type of colour blindness. A score below 10 means two colours are easily confused, 10 to 25 that they are separable but close, and above 25 that they are comfortably distinct.

    # A red and a green that look quite different to most viewers
    check_separation(c("#B7352D", "#627313"))
    # But not to everyone
    simulate_colorblind(c("#B7352D", "#627313"), "deutan")

Run the code, then try `"protan"` or `"tritan"` instead of `"deutan"`.

How far the simulation goes is set by `severity`. Full severity, the default, is dichromacy: deuteranopia, protanopia, tritanopia. A lower severity is anomalous trichromacy — deuteranomaly, protanomaly — which is the more common condition, and which the paragraph above named without being able to show you.

    simulate_colorblind(c("#B7352D", "#627313"), "deutan", severity = 1)
    simulate_colorblind(c("#B7352D", "#627313"), "deutan", severity = 0.4)

You can also look at a whole graph the way another viewer would, by mapping the simulated colours back onto it.

    graphr(fict_lotr, node_colour = "Race")
    graphr(fict_lotr, node_colour = "Race") +
      ggplot2::scale_fill_manual(values = simulate_colorblind(ag_qualitative(6), "deutan"))

Much of this work is already done for you. Each theme’s palette is reordered when the theme is set, so that the colours a graph uses first are the ones that stay distinct for every viewer, and each divergent palette pairs a warm pole with a cool one rather than a red with a green.

    stocnet_theme("iheid")
    round(check_separation(ag_qualitative(4)))
    # The closest pair among those four colours
    min(check_separation(ag_qualitative(4)), na.rm = TRUE)
    stocnet_theme("default")

Going further: The `"rainbow"` theme is the exception, and is left in the order of the spectrum, since that fidelity is its point. A spectrum is not a colour-blind safe scheme: its reds and greens are the pair that red-green colour blindness cannot separate. Choose it where the order of your categories is itself meaningful, and check the result with `check_separation()`. Where you need particular colours in an institutional palette, `match_color()` finds the closest the palette has to those you ask for.

### Greyscale

Other times colour may not be desired. Some publications require greyscale images, and a figure may be photocopied whether or not you meant it to be. A greyscale device keeps the luminance of a colour and throws the rest away, so two colours of the same lightness merge, however different their hues. This is why ColorBrewer marks a palette print-safe and photocopy-safe separately from marking it colour-blind safe: they are different questions, and a palette can pass one and fail the other.

`simulate_colorblind()` answers the second with `type = "grey"`, and `check_separation()` reports the greyscale distances beside its own score.

    check_separation(ag_qualitative(4))

The matrix is what every viewer can see. The line beneath it is what survives a photocopier. Most institutional palettes separate their categories by hue, so most of them collapse in greyscale.

To draw in greyscale from the start, replace `_hue` from above with `_grey` (note the ‘e’ spelling):

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

As you can see, greyscale 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 greyscale with `node_shape`, or use the `"bw"` theme, which is designed for this purpose. `stocnet_medium("print")` is the companion to this; see Where will it be seen? below.

### Manual override

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

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

### Where will it be seen?

A theme says how a plot should look. Where it will be seen is a separate question, and the answer changes more often than the theme does. The same institutional theme has to serve a figure worked on at a desk, projected in a lecture theatre, printed in an article, and read on a phone in a narrow column. Each of those wants a different size of text, and one of them wants a different background.

`stocnet_medium()` sets this, and leaves the theme alone.

    stocnet_medium()
    stocnet_medium("presentation")
    graphr(fict_lotr, node_colour = "Race")
    stocnet_medium("screen")

The media are `"screen"` (the default), `"presentation"`, `"mobile"`, and `"print"`. The first three differ in the size of their text; `ag_size()` reports the multiplier in force. `"print"` leaves the text alone and draws on white, whatever ground the theme prefers, since a dark or tinted ground costs ink and is often not reproduced. As with `stocnet_theme()`, `persist = TRUE` remembers your choice.

The medium scales text, not marks. A node’s size is relative to the layout it sits in, so enlarging the nodes without enlarging the layout would only crowd it. Use `node_size` in `graphr()` where a figure needs larger nodes too.

Nor does the medium set the size of the file you write. Give `ggsave()` the width, height, and resolution to match; see Exporting plots below.

Going further: A small figure limits how much it can carry, not just how large the type is. Keep a legend to about seven keys, and `graphs()` to about three panels. `graphr()` says so when a colour or shape legend grows past that, because past it a reader stops matching keys to marks and starts guessing. Splitting one crowded figure into two that each make a single point is almost always better than shrinking the type until it fits.

In brief: `stocnet_theme()` sets a theme once for all subsequent graphs and plots, with institutional and stylistic palettes included, and `persist = TRUE` keeps it for future sessions. 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 `simulate_colorblind()`, `check_separation()` and `check_contrast()` check that your palette works for colour-blind viewers, in greyscale, and as text. `stocnet_medium()` then sizes the result for where it will be seen.

## 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()` adds node labels. If you do not want any labels, you can remove the names 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.

But this is not really a choice between all and nothing. `fict_lotr` has 36 nodes, and 36 labels would cover the very network they describe, so `graphr()` labelled only the handful of most central characters and told you so. Ask for all of them with `labels = TRUE` and compare.

    graphr(fict_lotr, labels = TRUE)

You can decide how many to label by passing a number. This is a depth of ranks rather than a count of nodes, so characters tied at the cut are labelled together — ask for the top three and you may get four names.

    graphr(fict_lotr, labels = 3)

Degree is only one reason a node might be worth naming. Passing the name of a measure labels whichever node or nodes it singles out: `"betweenness"` for the characters who sit between others, `"cutpoints"` for those holding the network together, or `"random"` for a small unbiased sample.

    graphr(fict_lotr, labels = "betweenness")

To combine the two, name the number: `labels = c(betweenness = 5)`. And when you know exactly who matters to your argument, you can just say so — by name, or with any logical vector of the nodes.

    graphr(fict_lotr, labels = c("Frodo", "Gandalf")) +
      ggtitle("Named outright") |
      graphr(fict_lotr, labels = node_is_cutpoint(fict_lotr)) +
      ggtitle("Every cutpoint")

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). On a two-mode or multilevel network, a selection is ranked within each mode or level, so that a dense level cannot crowd the others out of the labelling.

### 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”.

    # Fill in the blanks (this is a template, not runnable code):
    # graphr(fict_lotr) +
    #   labs(title = _____, subtitle = _____)

    graphr(fict_lotr) +
      labs(title = "My visualisation",
           subtitle = "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

A legend asks a reader to hold a colour in mind while they hunt for it in the graph, and people are poor at that: colour is not recalled reliably, even over a couple of seconds. Labelling nodes directly asks less of them, which is why `graphr()` labels nodes where it can, and why, above thirty nodes, it labels the most central ones rather than none at all (see Labels above). Keep a legend for what cannot be written onto the graph itself, and keep it short.

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_colour = "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_colour = "maxbet") +
      guides(colour = "legend") +
      labs(colour = "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` chooses which nodes to name — all of them, none, the top few by a measure, or the ones you name yourself — 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.

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 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 ordering.

`{autograph}` offers four, and they are one layout drawn four ways. Two things vary: which axis the layers run along, and whether the nodes line up across them. The names say which is which — a railway lies flat, a ladder stands up:

| Layers stacked flat | Layers standing up
--- | --- | ---
Nodes spaced by their ties | `"layered"` | `"lineage"`
Nodes lined up across layers | `"railway"` | `"ladder"`

    graphr(ison_southern_women, layout = "bipartite") + ggtitle("Bipartite")
    graphr(ison_southern_women, layout = "layered") + ggtitle("Layered")
    graphr(ison_southern_women, layout = "railway") + ggtitle("Railway")

Note that `"layered"` and `"railway"` use a different algorithm to `{igraph}`’s `"bipartite"`, and generally perform better, especially where there are multiple layers. Whereas `"layered"` 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 `"layered"` 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 = "layered", center = "events")

If you want to flip the horizontal and vertical, you could flip the coordinates, or use `"lineage"`, which is the same layout with the axes exchanged.

    graphr(ison_southern_women, layout = "lineage") + ggtitle("Lineage")

These layouts serve both multimodal and directed acyclic networks. A genealogical network offers the clearest case: every tie points from an earlier generation to a later one. Where a force-directed layout obscures this ordering, `graphr()` uses the `"layered"` layout to make it clear. Draw the parent ties among the characters of Westeros.

    thrones <- to_uniplex(fict_thrones, "parent")
    graphr(thrones)

This layout tries to minimise two costs. The first is which layer each node goes in. Ranking each node by its distance from a root sounds right — a row is then a generation — but it pins a parent whose only child is born several generations later to the top row, and manufactures a long tie to reach them. The `ranks` argument chooses the rule, and `check_span()` reports how many rows each tie crosses, so you can measure the difference.

    thrones <- to_uniplex(fict_thrones, "parent")
    spans <- sapply(c("generation", "compact", "tight"), function(r) {
      span <- check_span(graphr(thrones, ranks = r))
      c(total = attr(span, "total"), `over one row` = mean(span > 1), max = max(span))
    })
    round(t(spans), 3)

`"generation"` is the distance-from-a-root rule and `"compact"` is the one `{igraph}` uses in its Sugiyama layout. `"tight"`, the default, minimises total tie length while still pointing every tie down at least one row. Note that the longest tie is the same under all three.

The second cost is where each node sits within its row. `check_offset()` reports how far each tie travels sideways, as a share of the width of the drawing, so a tie that drops straight down scores zero. Again, you are wanting to minimise this, and the `alignment` argument chooses the rule. Compare the two alignments.

    thrones <- to_uniplex(fict_thrones, "parent")
    c(straight = attr(check_offset(graphr(thrones)), "mean"),
      rungs = attr(check_offset(graphr(thrones, alignment = "rungs")), "mean"))

`alignment = "rungs"` gives every row the same spacing, which is what `"railway"` and `"ladder"` are for. The default, `"straight"`, pulls each node towards its parents and children instead, which is what makes the families read as families.

`ranks` also accepts a node attribute, instead of one of those three rules. Then the layers are that attribute’s values, and nodes are placed along the axis in proportion to them rather than at even steps, so a network of dated nodes is drawn as a timeline. Rank the adolescents by a year of your choosing.

    ison_adolescents |> as_stocnet() |>
      mutate_nodes(year = rep(c(1985, 1990, 1995, 2000), times = 2),
                   label = paste0(label, " (", year, ")")) |>
      graphr(layout = "lineage", ranks = "year")

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 exaggerate the clustering of similarly located nodes and separate less similar nodes in two-dimensional space.

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

#### Multidimensional scaling

Of similar purpose are multidimensional scaling (MDS) techniques, which visualise the similarity between nodes in terms of their proximity in a two-dimensional (or more) space. The `"scaling"` layout places the nodes so that the distance drawn between them stands for the number of steps between them in the network.

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

Note that this layout is drawn with the axes labelled, whereas you may have noticed that the other graphs are not. That is because here the coordinates can be read: two nodes drawn twice as far apart are, more or less, twice as far apart. The axes are drawn on one scale for the same reason. The layout scales the whole network where it is small enough for that, using `"mds"` from `{igraph}`, and otherwise approximates the scaling from a sample of the nodes using `"pmds"` (or pivot MDS) from `{graphlayouts}`. You can still call each of these directly, but since they are both used in `"scaling"`, dispatch can be automatic, based on the size and structure of the network.

“More or less” is doing some work in that sentence. A network usually has more structure than two dimensions alone can hold, so some of the distances drawn won’t capture the real distances in the network. In some cases, the dimensionality is so high that the drawing is misleading. We can check how much disagreement there is between scaled distances and the network distances as a stress score. This is printed as a caption under the plot as a percentage of the network distances, such that zero would represent a perfect drawing.

How low is low? Kruskal (1964), who introduced the score, recommends 20% as poor, 10% as fair, 5% as good, and 2.5% as excellent. Those figures were established for psychometric data though. Networks typically contain a lot more structure, which is hard to capture in just two dimensions, so a 20% threshold is often too demanding.

For networks, a score near 30% is quite common, and means the clustering can be interpreted though perhaps the distances should not be interpreted as exact. Above 40% and the plot does not really show any interpretable structure; `graphr()` will alert you in the console where the score is above 30%. By contrast, a stress score near 5% is rare and worth trusting.

Note that this stress score is not only for this layout. `check_stress()` measures any drawing the same way, so layouts can be compared on the same network (Brandes and Pich 2007):

    sapply(c("scaling", "stress", "fr", "circle"),
           function(x) check_stress(graphr(ison_southern_women, layout = x)))

The default `"stress"` layout scores a little better here, which is no accident: it minimises a related criterion directly. What `"scaling"` adds is the axes and the score, so that the distances can be read and the reading can be checked.

In addition to stress, the scaling layout also reports how much of the variance in the network’s distances the two dimensions drawn hold. The two numbers answer different questions, and the comparison above shows how. Stress belongs to the drawing: draw this one network four ways and you get four different scores. The variance explained belongs to the network: it is the same 31% whichever of the four you draw, because it asks how much of the structure two dimensions could hold at all.

So read them together. A low variance explained sets a floor that no layout gets under. Where two dimensions can hold only a third of the structure, no arrangement of the nodes will draw the distances faithfully, and stress tells you how close to that floor this particular drawing gets.

#### Correspondence analysis

Whereas scaling lays out nodes by their distances from each other, correspondence analysis (CA) lays them out by the similarity of their ties. This is useful where nodes may not be tied to each other at all, but can be tied to the same others, such as in a two-mode network. Correspondence analysis takes a rectangular table — here the incidence matrix of the Southern Women dataset, one row for each woman and one column for each event — and places its rows and its columns in one space.

    graphr(ison_southern_women, layout = "correspondence") + ggtitle("Correspondence Analysis")

We can see the similarity to the eigenvector layout above, but the axes are labelled with the share of the network’s inertia they hold. Inertia is the CA analogue of variance in PCA. It measures the total dispersion of points (rows and columns) in the cloud around the centroid, computed as the chi-square statistic of the table divided by the total sample size (N). In other words, inertia tell us how far the ties depart from what one would expect if every woman attended events in the same proportion as every other. A network whose nodes all had much the same ties would have almost none.

Each dimension extracted captures a share of this total inertia. Because it is a share of variance explained, and not a measure of fit like regression’s R-squared, the scores depend on the number of dimensions. `ison_southern_women` has 12 dimensions, and a total inertia of 1.65. The top two dimensions (in terms of variance explained) together account for 57% of this total inertia.

Is this good? I.e. is this a presentation of the data that is worth interpreting? Well, if the inertia were spread evenly across these 12 dimensions, (any) 2 dimensions would jointly account for about 17% of the variance. 57% is about 3.4 times better than this. But this flatters because inertia is never spread evenly (Jackson 1993). The broken stick model offers a more demanding baseline, asking what two dimensions would hold if the inertia were divided randomly rather than evenly (here 1.3 times better):

    ##               ison_southern_women ison_adolescents ison_networkers
    ## dimensions                  12.00              7.0           31.00
    ## inertia_drawn                0.57              0.6            0.36
    ## vs_even                      3.40              2.1            5.60
    ## vs_random                    1.30              0.9            1.60

`ison_adolescents` looks the best summarised by two dimensions of three datasets considered at 60%. However, it is a small network with only seven dimensions to spread across, so two of them were always going to hold a good deal. Against the harder baseline it scores below 1, which is to say two dimensions hold less than dividing the inertia at random would have given them. By comparison, `ison_networkers` looks the worst at 36% and yet summarises best: it has 31 dimensions, and the top two beat either baseline. Note that these scores are not verdicts, but help gauge whether the two dimensions presented are worth interpreting further. `graphr()` applies the stricter of the two baselines for you, noting at the console where two dimensions hold no more inertia than a random division would have given them.

Since the two dimensions have different percentages here, we can see where we should put the emphasis of our interpretation. Because the first dimension holds twice as much, it suggests that what distinguishes nodes most runs along the x-axis rather than the y-axis.

Two more things to note about correspondence analysis. First, while the distances among nodes of the same mode are interpretable, distances between nodes from different modes are not necessarily interpretable. That is, a woman drawn near an event is not necessarily an attendee of it. Only the distances within a mode can be read this way: two women drawn together attended similar events, and two events drawn together were attended by similar women. These plots are often misread this way.

Second, some nodes are better represented by the top two dimensions than others. A plot can hold most of the network’s inertia and still put one particular node nowhere near where it belongs. This representation is captured by a measure called cos2: how much of its position the two dimensions drawn actually hold, from 0 to 1, where lower is worse. A node the plane captures badly may be located near the centre of the plot, not because it is average, but because there is nowhere else to put it. `graphr()` names these nodes in the console when it draws the layout, but you can recover the scores like so:

    fit <- attr(layout_correspondence(ison_southern_women), "fit")
    round(sort(fit$cos2), 2)

For a directed network, each node has two profiles: who it sends ties to, and who it receives them from. By default the layout reads a tie in either direction, so that each node has one position; `direction = "out"` and `direction = "in"` read one profile or the other. For a signed network there is no correspondence analysis at all, since the method divides by the mass of each node and a negative tie has no such reading. `double = TRUE` splits each tie into a positive and a negative part, so that a node is placed by both who it likes and who it dislikes.

### 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"`, `"correspondence"`, `"levels"`, `"matching"`, `"scaling"`, `"valence"`, and the layered family — documented at `?layout_layered` and friends. Several layouts take a layout-specific extra argument (passed through `...`) to control how nodes are ordered: `"concentric"` a `membership`, `"levels"` a `level`, and the layered layouts `ranks` — 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 (`"layered"`, `"railway"`, `"lineage"`), circular (`"concentric"`, `"circle"`), spectral (`"eigen"`, `"scaling"`, `"correspondence"`), and grid layouts. Force-directed layouts are illustrative — do not over-interpret distances; spectral/MDS layouts place nodes by measured similarity, and `"scaling"` captions the plot with how far that reading can be trusted; 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.

    # Fill in the blanks (this is a template, not runnable code):
    # graphr(_____) + graphr(_____)
    # graphr(_____) / graphr(_____)

    graphr(fict_lotr) + graphr(ison_algebra)
    graphr(fict_lotr) / graphr(ison_algebra)

### 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, or select a few as in `graphr()`, which is resolved once so the same nodes stay named in every frame).

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_colour = "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_colour/node_shape/node_size/node_group)` | maps node attributes to aesthetics
`graphr(..., edge_colour/edge_size)` | maps tie attributes to aesthetics
`graphr(..., labels, label_repel, label_dist)` | chooses which nodes to label, and places the labels
`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, backbone, 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:

Backbone
  The backbone of a network comprises the ties that carry more weight, or hold more structure, than a null model local to their endpoints expects.
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.

# Visualising Networks

#### by James Hollway