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<p>Graphs are all around us. From railway networks to puzzle states it is clear that they are an integral type of data. However when it comes to machine learning, standard neural networks do not account for structural information which is key when learning on graph-structured data. Graph neural networks circumvent this problem by learning both structural and spatial biases from the input graph. In this workshop, we will be exploring how Graph neural nets work and their different applications!</p>
