domains / families / science / torch-geometric

torch-geometric

combined

Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning. Supports PyTorch Geometric (PyG) graph neural networks — node/link/graph classification, message passing (GCN, GAT, GraphSAGE, GIN), heterogeneous graphs, neighbor sampling, and custom datasets. Use when working with torch_geometric, not for general NetworkX analytics or non-graph PyTorch models.

When to use

• **Graph-based machine learning**: Node classification, graph classification, link prediction
• **Molecular property prediction**: Drug discovery, chemical property prediction
• **Social network analysis**: Community detection, influence prediction
• **Citation networks**: Paper classification, recommendation systems
• **3D geometric data**: Point clouds, meshes, molecular structures
• **Heterogeneous graphs**: Multi-type nodes and edges (e.g., knowledge graphs)
• **Large-scale graph learning**: Neighbor sampling, distributed training

Skill page

Conflict

The K-Dense-AI copy is a highly specific, version-pinned (PyG 2.8.0.post1, PyTorch 2.14.1) guide with critical warnings about extension wheels and isolated nodes. The other two copies (Vibe-Skills and OpenClaw) are nearly identical, generic guides that use outdated installation commands (installing deprecated `torch_cluster` and `torch_spline_conv`) and lack the specific version constraints. Because the K-Dense copy contains unique, critical safety/versioning steps that the others lack, and the others contain a broader (though outdated) set of model examples, crowning one would hide the other's content or propagate outdated installation advice.