Structured Video Understanding aims to generate structured representations of events occurring in a video. A common approach relies on Graph Neural Networks (GNNs) trained on scene graphs describing the entities and relationships present in individual frames. By exploiting relational information, these models support downstream tasks such as action recognition, video question-answering and scene graph generation. However, a key architectural limitation of standard GNNs lies in their reliance on pairwise interactions, which may be insufficient to capture the complex dynamics underlying human activities. To address this limitation, we investigate architectures based on Higher-Order relationships, enabling models to learn more abstract representations derived from interactions among multiple entities considered jointly.