Scale-Free Networks: Why Fairness is not the Issue

Networks generally fall into two distinct structures – scale-free and random.   

Random network versus scale-free network

Random networks assume each node has an equal chance of being connected to another node, whereas scale-free networks have a small number of high-degree nodes (hubs) and a large number of small-degree nodes (Hoogduin, 2015).

Examples of scale-free networks are all around us: wealth distribution, STD transmission, and social network popularity.  Artist Erin Gallagher visualized the usage of #metoo on Twitter between October 15 and 17, 2017.  Her image clearly shows a scale-free network, with a small number of highly-connected hubs and the vast majority of users having only a few connections.

This is a common pattern for Twitter and other networks that are based on preferential attachments.  The popular accounts have more users, which amplifies their tweets so that they reach more people.  This higher level of exposure brings them even more followers (Robins, 2015).

Initially, scale-free networks may seem to go against the principles of democracy and fairness.  For example, if power is in the hands of a few instead of many there is danger the powerful will exploit the less powerful.  This is certainly how dictatorships and authoritarian regimes work.  However, there is a difference between power being artificially hoarded and power being unequally distributed.  Artificially rigging systems to disproportionately benefit those already in power is both unequal and unfair.  Disproportionality in blog popularity, as Clay Shirky discusses in Power Laws, Weblogs and Inequality, is unequal but largely fair.    

Scale-free networks are a fact of life – it does no good to argue whether or not they should exist (because they already do) or whether or not they are fair (because fairness depends on the context).  Instead, we should look at the result and impact of network distributions.  Are certain people or groups being disenfranchised or disproportionately hurt?  Is this inequality purposeful, or due to the cumulative impact of preferential attachments?  Purposeful inequality that disproportionately and negatively impacts people should be corrected.  Inequality that puts power in the hands of those who have historically been excluded (such as the citizen journalism Shirky discusses) can enhance freedom and democracy.  When the cause is unintentional preferential attachments, the decisions are more complicated.  Artificial network regulation may be necessary or new networks may need to be created to increase access to missing resources.   We must be careful to maintain a balance between the freedom of individual choices and the collective impact.

References

Hoogduin, L. (2015). Random and scale-free networks. Retrieved from https://www.youtube.com/watch?v=dNbSWsQGHsw&t=57s

Robins, G. (2015). Doing social network research: Network-based research design for social scientists. Thousand Oaks, CA: Sage.