Introduction to Graph Machine Learning
In this blog post, we cover the basics of graph machine learning. We first study what graphs are, why they are used, and how best to represent them. We then cover briefly how people learn on graphs, from pre-neural methods (exploring graph features at the same time) to what are commonly called Graph Neural Networks. Lastly, we peek into the world of Transformers for graphs. Graphs What is a graph?
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Introduction to Graph Neural Networks with NVIDIA cuGraph-DGL | NVIDIA Technical Blog
GitHub - alibaba/graphlearn-for-pytorch: A GPU-accelerated graph learning library for PyTorch, facilitating the scaling of GNN training and inference.
dgl/tutorials/dist/1_node_classification.py at master · dmlc/dgl
GitHub - dmlc/dgl: Python package built to ease deep learning on graph, on top of existing DL frameworks.
pytorch_geometric/examples/multi_gpu/distributed_batching.py at master · pyg-team/pytorch_geometric
pytorch_geometric/examples/multi_gpu/distributed_sampling.py at master · pyg-team/pytorch_geometric
Math Behind Graph Neural Networks
NVIDIA #GTC2024 Conference Session Catalog
