Recommender systems (RecSys) traditionally leverage the users’ rich interaction data with the system, but ignore the sequential dependency of items. Sequential recommender systems aim to predict the next item the user will interact with (e.g., click on, purchase, or listen to) based on the preceding interactions of the user. Current state-of-the-art approaches focus on transformer-based architectures and graph neural networks. Specifically, the modeling of sequences as graphs has shown to be a promising approach to introduce a structured bias into the recommendation learning framework. In this work, we will outline our research of exploring different applications of graphs in sequential recommendation.