pytorch-geometric
Overview
PyTorch Geometric (PyG) is built on top of PyTorch to simplify the implementation of Graph Neural Networks. It treats graphs as Data objects containing node features and edge indices, and provides a powerful MessagePassing base class for custom layer development.
When to Use
Use PyG for data that is naturally represented as a graph, such as social networks, molecular structures, or point clouds. Use it when you need to perform node classification, edge prediction, or graph-level regression.
Decision Tree
- Do you have a list of small graphs?
- USE:
torch_geometric.loader.DataLoaderto create a single giant disjoint graph.
- USE:
- Do you need to pool node features into a graph-level feature?
- USE:
global_mean_poolorglobal_max_poolusing thebatchvector.
- USE:
- Are you building a custom convolution?
- INHERIT: From
torch_geometric.nn.MessagePassing.
- INHERIT: From
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