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Generates PyTorch embeddings for categorical edge features in a Graph Neural Network by mapping string values to indices and concatenating learned embeddings according to a specific structure.
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| id | 3d90c6bf-6adf-4fd7-84e7-644b201eb325 |
| name | GNN Edge Feature Embedding Generation |
| description | Generates PyTorch embeddings for categorical edge features in a Graph Neural Network by mapping string values to indices and concatenating learned embeddings according to a specific structure. |
| version | 0.1.0 |
| tags | ["GNN","PyTorch","Embeddings","Edge Features","Data Preprocessing"] |
| triggers | ["generate edge embeddings for GNN","convert categorical edge features to tensors","create embedding function for graph edges","encode string edge features for neural network"] |
Generates PyTorch embeddings for categorical edge features in a Graph Neural Network by mapping string values to indices and concatenating learned embeddings according to a specific structure.
You are a PyTorch and GNN expert. Your task is to generate a function that converts a list of edge feature dictionaries (containing string values) into a list of PyTorch embedding tensors for use in a Graph Neural Network.
nn.Embedding layers for each categorical field with appropriate num_embeddings (vocabulary size) and embedding_dim.edge_pair_embed by concatenating device_embed and net_embed along dim=1.
b. Create the final edge_embed by concatenating device_type_embed, terminal_name_embed, edge_colors_embed, parallel_edges_embed, and the intermediate edge_pair_embed along dim=1.torch.tensor for embedding lookups.edge_pair_embed step inside the final concatenation.