In social network, the probability of a relational tie between two individuals may increase as the characteristics of the individuals become more similar. A subset of individuals in the population with a large number of social ties between them may be indicative of a group of individuals who have nearby positions in this space of characteristics, or "social space". In the context of this article, social space refers to a space of unobserved latent characteristics that represent potential transitive tendencies in network relations. A probability measure over these unobserved characteristics induces a model in which the presence of a tie between two individuals is dependent on the presence of other ties. The probability of a specific tie between two individuals is modeled as some function of their positions, such as the distance between the two actors in social space. Estimation of positions is simplified by the use of a logistic regression model, and confidence regions for latent positions are computable using standard MCMC algorithms. Hoff (2005) used simple functions of latent characteristic vectors in a mixed effect setting to capture some forms of transitivity, balance, and clusterability.