torch-geometric

To build a graph neural network for node classification in PyTorch Geometric, you structure your data with correct edge_index formatting, select layers like GCN or GraphSAGE, and define explicit activations. This Skill guides you through designing and training these complete model workflows.

Training large-scale graph neural networks requires neighbor sampling, link sampling, or clustering loaders. This Skill helps you implement scalable training workflows for large graphs that exceed memory limits by configuring appropriate mini-batch sampling strategies in PyTorch Geometric.

Message passing for heterogeneous graphs handles multi-type nodes and edges using architectures like HGT. This Skill allows you to build heterogeneous graph models for recommendation systems and knowledge graphs by defining type-specific transformations and message passing functions in PyTorch Geometric.

Yes, you can convert NetworkX graphs or CSV data into custom PyTorch Geometric datasets. This Skill covers creating custom datasets, formatting edge_index correctly, and transforming raw graph data into trainable representations for your graph neural network workflows.

To interpret graph neural network predictions, you use GNN explainers to identify the most influential subgraph for a specific prediction. This Skill provides workflows for explainability, allowing you to understand which graph structures drive your model's outputs.

Yes, building link prediction models requires version-compatible PyTorch Geometric dependencies and correct edge_index formatting to avoid errors. This Skill helps you manage explicit activations and dependency versions for stable link prediction and message passing workflows.