E-learning occupies an increasingly prominent place in education. It provides the learner with a rich virtual network where he or she can exchange ideas and information and create synergies through interactions with other members of the network, whether fellow learners or teachers. Social network analysis (SNA) has proven extremely powerful at describing and analysing network behaviours in business, economics and medicine, but its application to e-learning has been relatively limited. This systematic review of the literature on SNA in e-learning aimed to assess the evidence for using SNA as a way to understand and improve e-learning systems, as well as suggest directions for future research. Most of the 37 studies included in this review applied various methods to analyse interaction patterns in forums involving one-mode networks. Indices of centrality and density were the SNA measures most often used. Although the small number of included studies means that our systematic review should be considered preliminary, the evidence so far strongly suggests that SNA, particularly when combined with content analysis, can provide a detailed understanding of the nature and type of interactions between members of the network, allowing for optimisation of course design, composition of learner groups and identification of learners in danger of dropping out. Future studies should examine two-mode networks and communication channels like chat rooms, wikis, blogs and microblogs. Whenever possible, future studies should also include a quantitative approach that exploits the statistical power of SNA to explain complex systems.