  Music genre is arguably one of the most important and discriminative
information for music and audio content. Visual representation based approaches
have been explored on spectrograms for music genre classification. However,
lack of quality data and augmentation techniques makes it difficult to employ
deep learning techniques successfully. We discuss the application of graph
neural networks on such task due to their strong inductive bias, and show that
combination of CNN and GNN is able to achieve state-of-the-art results on
GTZAN, and AudioSet (Imbalanced Music) datasets. We also discuss the role of
Siamese Neural Networks as an analogous to GNN for learning edge similarity
weights. Furthermore, we also perform visual analysis to understand the
field-of-view of our model into the spectrogram based on genre labels.
