Common assumptions about networks should be questioned
Steadiness and continuous evolution?
Most analyses of the social structure of a network implicitly assume that the relationships in the network are relatively stable. This assumption follows directly from theories of network evolution that posit that networks grow monotonously, with links added sporadically around the network. One of the main forces that drive network evolution is assumed to be the network itself. For instance, people that have many common friends tend to develop ties with much higher probability than the ones that don’t. We are used to viewing network growth as occurring at a regular, monotonous pace and do not expect networks to change radically over time. While monotonous-like patterns of network evolution seem reasonable, there is little empirical evidence to support that assumption.
Another trait that is assumed in a strong way, perhaps implicitly, is the stability of similarity between connected individuals, or homophily in academic terms. This similarity is one of several principles that are typically invoked to explain network evolution. It implies that people tend to connect to people who resemble them: the “birds of the feather flock together” principle. New links are likely to be formed when individuals share some common traits. As a result of this principle in action, a network typically contains clusters made up of similar people, and peer influence can drive connected people to become more similar.
How similar is similar?
Another critical issue of many network studies is how they measure similarity. Most studies compile a very limited set of parameters such as age, gender, political affiliation, community membership, etc. into some similarity measure. These are easy to measure, but, even if taken together, these variables represent an unusually small number of aspects of human existence and a very superficial and mostly context-independent view of what makes people similar or different. We are not sure whether these are even the features that have the greatest impact in motivating people to connect to others. They are simply the information that is most easily collected. Nonetheless, we have been generalizing from such limited sets of traits to determine that two individuals are similar in a way that is meaningful for network evolution. Further, previous studies typically make no connection between two important parameters of network evolution: the interrelation of similarity effects and structural evolution.