Network Analysis in Global Politics
What do a slice of Swiss cheese, a Magnetic Resonance Imaging (MRI) scan, and global politics have in common? Complexity. Swiss cheese’s appearance changes by slicing in different directions—depending on the cut the same holes look different. An MRI takes detailed pictures of structures such as the human brain. Tiny two-dimensional slices are assembled to provide an extremely accurate three-dimensional depiction of multipart reality, but any one slice provides only a partial picture that is accurate but can be misleading. Finally, global politics has a tremendous number of actors: countries, people, companies, charities, and so on, all interacting. This complex structure of world politics can be studied by examining slices at different geographical, temporal, and issue junctures. One way to take a “slice” of geopolitics is to look at the networks that the actors form.
People talk about networks all the time. It’s a cliché to say that the world is connected; that it’s a “small world.” But not all connections are the same. In the game “Six Degrees of Kevin Bacon,” players have to connect any actor named to the actor Kevin Bacon in six steps or less. For example, Hollywood and Broadway legend John Barrymore was in “The Great Profile” (1940) with Marc Lawrence, who was in “Blood Red” (1989) with Elias Koteas, who was in “Novocaine” (2001) with Kevin Bacon. So, Barrymore to Bacon in three steps, which makes the world seem very small indeed. But think about that—how meaningful are those connections? They never met; in fact, Barrymore died before Bacon was even born.
This holds true even for people who share the same time and space—I know people who know people who know the president of the United States. Chances are you do too. But for most of us, this still means that our impact on presidential policy is zilch. Why? The reason is that a network analysis would show that our social networks are not randomly connected—some nodes are much more likely to have lots of connections than others. Furthermore, some ties are stronger or more important than others. Understanding how networks work is the first step to understanding our global society.
Network analysis is a way to rigorously study “slices” of global political structure. Studying “slices” of global politics could help identify gaps. They could illustrate how different actors work in tandem or at cross-purposes on the same problem. And by putting them together, we can get an idea of structure that is digestible. But not all MRI-like slices will provide the same picture of the evolution in international society because the patterns of relationships and institutions vary by issue, geographic location, and historical period. For instance, calculations about international peace and security are not the same for major and minor powers, not similar for nuclear weapons and drugs, and not comparable before and after 9/11. Network analysis may also reveal properties of networks that are not visible through other methodologies—such as behavior patterns that spontaneously arise, like cooperation after a disaster.
Knowing the structure of the prevailing social forces allows actors more freedom to act: knowing where to influence is vitally important to being able to exercise influence at all.
Basic Elements of Network Analysis
The basic elements of network analysis are actors, relations, and attributes. Actors are whatever social unit you happen to be measuring: people, organizations, or things. Relations are the ties between or among actors that are channels for the flow of resources or ideas, such as money transfers or scientific information. (Relations can also be affiliational, meaning that people or things in a particular situation can be counted as a network tie, such as those attending an event.) Finally, attributes are measures taken on actors. These are measures familiar to most of us, such as demographic data—age, gender, income, geographic location, etc.
Network analysis also has some basic assumptions. First, it must involve dealing with relations. There must be some kind of connection between and among actors—it is not a method suited to studying units in isolation. You’re looking at forest, not individual trees (or individual species in the forest). Second, using network analysis assumes that the units are interdependent; that their ties both enable and constrain what actors can do. If you’ve ever listened to tweens explain the complex web of friends, enemies, and frenemies in a middle school, you’ll know that no one child achieves popularity on their own—it is a status that is collectively conferred and relative to all the other statuses. This aspect of network analysis means that we cannot use regular statistical methods, because the actors are not independent.
“Network” itself has two meanings: it refers to both structure and function. Actors form a network (structure) and also use the network they have formed (function). For example, you may join a professional group, which adds to its structure. You may then attend a “networking” event, in order to create a new structure of personal ties to people with whom you already share the professional tie. The study of networks is the study of system-level relational properties, called structure. This is not structure in the sense of a building or a road, but “enduring patterns of relations,” or interactions among actors that persist across space and time.
The basic measures of social network analysis are density, centrality, and cohesion, which are used to measure and compare the interactions in networks of actors. Networks measured in these ways can be compared and contrasted easily, and these comparisons can continue over time. Social scientists ask two big questions of every network: which are the most important actors? And are there subgroups of similar actors?