Egocentric vision is a domain of computer vision centered on video data captured from wearable devices such as head-mounted cameras. Videos from the user's viewpoint offer unique insights into human behavior and environmental contexts, with applications in augmented reality, activity recognition, and human-computer interaction. This thesis aims to develop a model to extract relevant features from egocentric videos exploiting labels constructed using scene graphs, which summarize the content of a given frame with verb-object-relationship triplets. Moreover, we propose a novel approach to the action anticipation task using graph-structured encoded data. We employ a Graph Neural Network (GNN) where visual features extracted from video frames serve as GNN nodes, while edges model the relationships between them.