mims-harvard/graphml-tutorials

Covers theoretical foundations and practical implementations of graph neural network architectures, from foundational concepts to domain-specific applications in scientific and biomedical prediction tasks. Built on PyTorch and PyTorch Geometric, providing end-to-end examples that demonstrate how to learn representations from arbitrary graph structures for downstream tasks like node classification and link prediction.

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