This study explored relations between social network characteristics in an online graduate class and two learning outcomes: affective and cognitive learning. The social network analysis data were compiled by entering the number of one-to-one postings sent by each student to each other student in a course web site discussion space into a specially designed spreadsheet. Regression analysis revealed that both network prestige and network centrality were robust predictors of cognitive learning outcomes. Self-reported affective learning, however, was not related to network factors. Results illustrate the utility of social network analysis in understanding interaction and learning outcomes in online classes.