torch-geometric

You can build a Graph Neural Network for node classification using PyTorch Geometric by representing graph data with the Data object and constructing models with its 60+ integrated GNN layer implementations tailored for node classification tasks.

Yes, you can train Graph Neural Networks on large graphs using mini-batches. PyTorch Geometric supports scalable mini-batch training, allowing you to process large graph structures efficiently without loading the entire graph into memory simultaneously.

The best way to handle heterogeneous graphs with multiple node and edge types is using the HeteroData object in PyTorch Geometric. It facilitates working with complex graph structures containing multiple node and edge types for diverse machine learning applications.

Yes, you need additional libraries besides PyTorch to use PyTorch Geometric for link prediction. You must install pyg-lib, torch-scatter, torch-sparse, and torch-cluster to support specific graph operations required for link prediction and other GNN tasks.

Yes, PyTorch Geometric supports graph classification tasks. It provides a comprehensive library with 60+ GNN layer implementations that can be applied to graph classification, enabling you to predict properties of entire graph structures.