torch-geometric

Graph neural networks classify nodes using GNN layers like GCN or GAT applied to graph-structured data. PyG provides pre-built layers, PyTorch integration, and datasets (e.g., Cora) to train end-to-end models for node classification tasks.

GCN, GAT, and GraphSAGE are distinct GNN architectures in PyG. GCN uses spectral convolution, GAT applies attention mechanisms, and GraphSAGE samples neighborhoods. Each suits different graph structures and learning objectives.

Yes. PyG supports mini-batch training and distributed training for large-scale graphs using DataLoader-based batching and edge_index COO format, enabling scalable GNN development beyond single-GPU memory limits.

PyG supports heterogeneous graphs and molecular property prediction through Data objects with edge attributes, node features, and custom GNN layers. Molecular tasks use graph classification on compound structures.

Graph neural network development with PyG requires Python 3.x, PyTorch, and torch_geometric installation. Optional dependencies include pyg_lib, torch_scatter, torch_sparse, torch_cluster, and torch_spline_conv for advanced features.

CUDA is optional for PyG. CPU execution is supported, but CUDA dependencies accelerate training on large graphs. Installation varies based on your hardware and PyTorch version.