Engaging students in peer assessment is an innovative assessment process which has a positive impact on students learning experience. However, the adoption of peer assessment can be slow and uncomfortably experienced by students. Moreover, peer assessment can be prone to several biases. In this paper, we argue that the analysis of peer assessment interactions and phenomena can benefit from the social network analysis domain. We applied a graphlet-based method to a dataset collected during in-class courses integrating a peer assessment platform. This allowed for the interpretation of networking structures shaping the peer assessment interactions, leading for the description of consequent peer assessment roles and their temporal dynamics. Results showed that students develop a positive tendency towards adopting the peer assessment process, and engage gradually with well-balanced roles, even though, initially they choose mostly to be assessed by teachers and more likely by peers they know. This study contributes to research insights into peer assessment learning analytics, and motivates future work to scaffold peer learning in similar contexts.