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Learn essential techniques for properly splitting graph datasets into training, validation, and test sets in this 17-minute tutorial that helps avoid common pitfalls when working with graph-based machine learning data. Explore practical examples using popular frameworks like PyG (PyTorch Geometric) and DGL (Deep Graph Library) while discovering key considerations specific to graph data structures that can impact model performance and evaluation.
