Table 1.

Glossary of terms

TermDefinition
Social-network theories
 Social contagion“The spread of affect or behaviour from one crowd participant to another; one person serves as the stimulus for the imitative actions of another.” (16) This includes sharing information, imitating behaviors, and enforcing norms.
 HomophilyThe tendency for people to seek out, or be attracted to, those who are similar to themselves.
 SocialityThe tendency for people to form relationships, commonly referred to as extroverted.
 ClusteringThe tendency for network members to share mutual relationships. For example, if member A is linked to member B and member C, it is likely that members B and C are also linked. This is also known as transitivity.
 CentralizationThe tendency for a few members to have many links while most other members have one or two links. For example, if member A joins a network and member C has five links and member B has one link, member A would preferentially form a link with member C. This is known as preferential attachment.
Social-network analysis
 LinkA social-network term for a relationship between two network members.
 EdgesAnother term for a link or relationship used in graph theory.
 DegreeThe number of relationships (links) a network member has.
 DensityHow many links exist between members of a social network out of the possible number of links that could exist among members.
 Dense networkIn a dense network, most or all of the members are linked to the other members.
 Sparse networkIn a sparse network, most members are not linked to the other members.
 STERGMA separable temporal exponential random graph model analyzes the network as a multivariate observation with a link as the dependent variable. The observed network is then compared to 100,000 randomly generated Markov random graphs (networks) using maximum pseudo-likelihood estimation and Monte Carlo maximum likelihood estimates (26).
 GWESPGeometrically weighted edgewise shared partner weights the probability of two members forming a relationship on the basis of the number of relationships with other members they have in common. This parameter is used in STERGMs to approximate clustering within the network.
 GWDegreeGeometrically weighted edgewise degree weights the probability of a network member forming a relationship on the basis of the number of relationships they already have (number of relationships=degree). This parameter is used in STERGMs to approximate centralization within the network.
  • STERGM, separable temporal exponential random graph model; GWESP, geometrically weighted edgewise shared partner; GWDegree, geometrically weighted edgewise degree.