Welcome to Lecture 6 in the undergraduate social networks course at the University of Maine at Augusta! This week, we delve further into the capabilities for working with social networks in the computer program R. We’ll learn how to measure node and network characteristics using R, and how to use the visual cues of color, shape and size to emphasize the properties of nodes and patterns in networks. But we also try to keep it real: for many years, social networks have been visualized outside the bounds of graph theory and social data analysis. The visualization of networks helps to keep us connected in our daily lives, which means that by understanding how networks are visualized, you gain an advantage in work, in culture, in life.
This week’s reading and lecture are meant to go hand in hand. As you review the lecture, be sure to have this week’s reading, “Measuring and Visualizing Networks in R,” at your side for reference. As you read that chapter, come back to this lecture to review the two video walkthrough guides; they’re designed to get you through the stickiest parts.
Reminder: Take-Home, Open-Book Exam Coming Up Next Week
Don’t forget: we’ve got an upcoming exam! This exam is a take-home, open-book exam, a chance for you to demonstrate that you’ve mastered the skills of social network analysis that we’ve covered so far in class (including this week’s material). I’ll be distributing the exam on October 16, and it will be due on October 22, giving you a fair amount of time to work through the tasks the exam will set out for you.
I’d like to offer an extra video of exam review preparation that I’ll compile and post on the morning of Sunday, October 15. Send any review questions you’d like to have answered in an exam review by e-mail to firstname.lastname@example.org by October 13, and I’ll put together a compliation of answers to whatever questions I receive into a video that I’ll post the next day. Don’t rely on your neighbor to ask your question for you! If there’s something about social network analysis that you don’t quite understand, ask.
Three Ways to Get Network Data into R
As this week’s reading notes, there are three fairly easy ways to load social network data into R, one involving typing and the other two involving basic spreadsheet programs like Microsoft Excel or Google Sheets. As you read through written explanations of how to do it, follow along in this video as I show you what actually going through the process should look like. Together, I hope the reading and the lecture video provide you with a path to mastering these techniques.
Visualizing Networks in R
To visualize a social network is to take an abstract mathematical entity and make it real to your eyes. No matter how many years of formal education we have, we cannot escape the brain power that is most humans’ birthright: to see objects and find patterns visually. Visualizing a social network is a way of using our power of sight to uncover social structure. This step in understanding a network is just as important as making technical measurements of that network.
The following video walkthrough is meant to accompany this week’s reading as a guide to exploring some of the different visualization options provided by the R program. As I hope you’ll see, you really can look at the same interaction from many different perspectives, each brought out by a different visualization technique.
Network Visualization is Everywhere: Maps as Network Graphs
Although matrices and network graphs have been used by academics to characterize patterns of social ties for some eighty years, they didn’t burst into popular consciousness as “networks” until the last decade or so. Still, once you begin to think of the world in terms of social networks, network imagery seems to appear everywhere, and many of those images predate the popularity of networks.
As you’ll recall from earlier in the semester, centuries ago Leonhard Euler came upon graph theory as a way of simplifying his daily walks through the city of Königsberg:
But the simplification of our travels through the physical world did not begin with Euler. Consider this detail from the “Tabula Peutingeriana,” a 1265 image of the roads of the Roman empire:
This section of the image, which shows modes of travel for Constantinople, Crete and Asia Minor, is not precisely a geographic map. Accurate depiction of waterways and landmasses have been sacrificed as secondary in order to highlight the information of primary importance: communities (which you might think of as the nodes of this network) and straight-line roads (which are ties connecting the communities nodes). The monk who drew the Tabula Peutingeriana wouldn’t have used the phrase “social network graph” to describe his image, but he was clearly thinking in a fashion parallel to that of modern network analysis.
In modern times, road atlases like the one you see below pay more attention to the precise location of some features — parks, highways, communities and bodies of water — in physical space. There’s even an inclusion of a scale key so that the physical distance between and along these features can be measured in miles. But even here, you’ll notice that complete physical accuracy has been sacrificed in order to highlight relational information. The actual appearance of Idaho and surrounding areas is obscured: mountains and plains have been painted over in blocks of white, or tan, or brown. Smaller communities and smaller roads are not included on the map. The information communicated here pertains the largest communities (our “nodes”) and the busiest roads (the strongest ties) are indicated, so that those traveling from one region of Idaho can take the shortest drive (or network “walk“) from one place to another.
A modern transportation map fully corresponding to a network sociogram is the following map of the “London Underground,” the subway system beneath the capitol city of the United Kingdom. Once a traveller heads underground, they can no longer see geographic features, and as passengers rather than drivers of the Underground cars the twists and turns taken by the cars are irrelevant; only origins, destinations, and connecting stations need to be known in order for people to get from place to place. Want to be communicated across the Underground’s transportation network? Just follow the lines between the nodes. For an equivalent network map in the United States, check out this graphic for the DC Metro.
What’s really interesting is that abstract maps like interstate atlases are confirmed through measurement of our collective behavior. Consider the Vulcan Project of Purdue and Arizona State Universities, which tracks carbon dioxide emissions across the United States. In the video below, Vulcan Project visualizers have directly mapped CO2 emission, indirectly mapping population centers and transportation routes that produce carbon dioxide. This CO2 map is a social network map of nodes and ties between the nodes.
Taking the network insight regarding maps down a level in scale from the inter-city level to the within-city level, the City Form Lab at the Massachusetts Institute of Technology has released a software tool for urban network analysis in physical space, to be used with the (unfortunately not free and open source) ArcGIS software suite. I don’t expect you to use this software, but I do want you to learn about it and see what kind of practical applications are possible for analyzing social networks across physical space. The skills to which you’re being introduced in this class can be instrumental in a professional career, as you’ll see. Please watch this short video:
Kozo Sugiyama’s Rules of Visualization: Iron-Clad?
In a series of publications spanning his career, graph theorist Kozo Sugiyama attempted to develop a series of rules about how to create network graphs that are easy to read and interpret. Later in life, he became ever more baroque, even developing a series of rules for making rules about graphs. See the “References” section below for citations of books and articles in which Sugiyama discusses his reasoning behind these rules, a few of which are:
1. Lines should be straight.
2. Lines should be far apart from one another.
3. Lines should not cross or touch.
4. Lines should be easy to follow from one node to another.
5. Nodes that connect should be close.
6. Nodes that are most central should be close to the center of the graph.
7. Nodes that are similar in some way should be placed nearby one another.
You may want to think about some of Sugiyama’s rules as you work through the various and sometimes opposing visualization options presented by R, then use them to complete your homework this week. Sugiyama’s rules are not really so iron-clad as laws of physics, but rather are flexible principles of design that embody some (and only some) assumptions about what network graphs are supposed to represent. Even in the most “beautiful” images of social networks, you’ll some of Sugiyama’s rules bent or broken. As our ideas about what images represent change, the rules of network design should be expected to change as well.
Lecture Participation: Find and Assess a Network Visualization
It’s time to get participatory again! For your in-course lecture participation activity this week, I’d like you to head out onto the internet (Google Images at http://images.google.com is a good place to start) and find an image of a network graph from somewhere out there in the Internet. Pin your image onto the Padlet below by clicking the appropriate “add an attachment” icon…
… and entering the URL (web address) of that image. Then review the image in terms of the quality of the visualization: does the visualization follow Kozo Sugiyama’s guidelines? Regardless of whether it follows Sugiyama’s rules, do you think the network graph is a good/sensible/useful visualization or a poor/confusing/useless one? Why is that?
(It’s best to use the Padlet, because the format lets us see one another’s work and thereby learn from one another. But if you absolutely can’t get the Padlet to work, please send your contribution to me by e-mail at email@example.com.)
Homework: Visualize Your Family
Homework #5 is coming up, due Sunday October 15, and it is a chance for you to harness the skills demonstrated in this week’s readings and video guides. Try them yourself and make them your own! Here’s the task:
- Use R to measure as many node and network characteristics as you can for the family network you created for Homework #4. Include node labels for each family member.
- Use color, shape and size to effectively visualize the individual, node and network characteristics of your family network. Save the resulting image to your computer.
- Include all characteristic measurements and the image from the steps above in a word processing document. In that word processing document, describe and explain the choices you made when visualizing your network. Upload the document to the appropriate Blackboard Homework Assignments section labeled “Homework #5.”
Best of luck — I hope that by the time you’re done with this week’s R homework you’ll feel that the program is a bit more like yours.
Friedkin, Noah. 1983. “Horizons of Observability and Limits of Informal Control in Organizations.”Social Forces 62(1): 54-77.
Sugiyama, Kozo, Shojiro Tagawa and Mitsuhiko Toda. 1981. “Methods for visual understanding of hierarchical system structures.” IEEE Trans. on Systems, Man, and Cybernetics SMC-11(2): 109-125.
Sugiyama, Kozo. 1987. “A cognitive approach for graph drawing.” Cybernetics and Systems 18(6): 447-488.
Sugiyama, Kozo. 2002. Graph Drawing and Applications for Software and Knowledge Engineers. Singapore: World Publishing Company Inc.