torch-geometric

You can build and train Graph Neural Networks using PyTorch by utilizing pre-built layers like GCN, GAT, and GraphSAGE, or by constructing custom layers for node and graph classification tasks. It provides data loading, model building, and evaluation pipelines.

You can analyze various graph-structured data, including citation networks, molecular graphs for drug discovery, and social networks to predict user behavior. The framework handles data loading and processing for these complex structures to facilitate deep learning.

You can implement node and graph classification tasks on heterogeneous graphs by using the provided training and evaluation templates. The framework supports heterogeneous graph structures alongside advanced features like explainability and pooling.

Yes, you can use custom GNN layers or pre-built ones like GraphSAGE for molecular property prediction. The framework allows you to load molecular graphs and execute training pipelines to predict properties for drug discovery.

Yes, the framework supports advanced features including explainability and pooling. These features are integrated alongside custom layer development and heterogeneous graph support to enhance model training and evaluation.

Yes, PyTorch is a required dependency. You can install PyTorch Geometric via `uv pip install torch_geometric` to start building models for analyzing complex graph-structured data.