The majority of Music Information Retrieval (MIR) research is Western-centric, and the limited availability of annotated resources poses a challenge for data-intensive approaches. In this work, we implement data-driven models and analyse their classification performance in two fundamental concepts in Arab-Andalusian music: nawba and ṭāb‘ using symbolic encoding. To address data scarcity, we employ two data augmentation strategies: sliding window segmentation and graph sub-sampling. We process a dataset of Arab-Andalusian digital scores to extract meaningful symbolic features and provide the resulting dataset for experiment reproduction and further research. Our results show that data-driven Machine Learning approaches provide a significant improvement for the aforementioned classification tasks compared to model-based Artificial Intelligence. Moreover, we introduce a method based on a Graph Convolutional Neural Network (GNN) architecture that exploits the relationships between music components. To the best of our knowledge, this is the first application of a GNN to Non-Western MIR. This work has the potential to set a new baseline for state-of-the-art methods which identify nawba and ṭāb‘.