The first goal of the analyses was to evaluate whether an individual's alcohol consumption behavior was associated with that of his or her social network ties at various degrees of separation. To test this hypothesis, the observed clustering of people (and their alcohol consumption behavior) within the whole network was compared to 1,000 simulated networks with the same network topology and the same overall prevalence of drinking as the observed network, but with the incidence of drinking (defined, say, as drinking at least one drink per day) randomly distributed across the nodes (“random drinking networks”). If clustering in drinking behavior is occurring, then the probability a contact is a drinker given that a principal is a drinker should be higher in the observed network than in the random drinking networks (21). We used the Kamada-Kawai algorithm to draw the networks; this algorithm iteratively repositions nodes in order to reduce the number of ties that cross each other. (22)
