Title: The perceived assortativity of social networks: Methodological problems and solutions,
Abstract: Networks describe a range of social, biological and technical phenomena. An
important property of a network is its degree correlation or assortativity,
describing how nodes in the network associate based on their number of
connections. Social networks are typically thought to be distinct from other
networks in being assortative (possessing positive degree correlations);
well-connected individuals associate with other well-connected individuals, and
poorly-connected individuals associate with each other. We review the evidence
for this in the literature and find that, while social networks are more
assortative than non-social networks, only when they are built using
group-based methods do they tend to be positively assortative. Non-social
networks tend to be disassortative. We go on to show that connecting
individuals due to shared membership of a group, a commonly used method, biases
towards assortativity unless a large enough number of censuses of the network
are taken. We present a number of solutions to overcoming this bias by drawing
on advances in sociological and biological fields. Adoption of these methods
across all fields can greatly enhance our understanding of social networks and
networks in general.