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Found 2 papers, 1 papers with code
pyGSL: A Graph Structure Learning Toolkit
Implementations of differentiable graph structure learning models are written in PyTorch, allowing us to leverage the rich software ecosystem that exists e. g., around logging, hyperparameter search, and GPU-communication.
Learning Graph Structure from Convolutional Mixtures
Machine learning frameworks such as graph neural networks typically rely on a given, fixed graph to exploit relational inductive biases and thus effectively learn from network data.
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