torch-geometric-graph-neural-networks
PyTorch Geometric (PyG) for graph neural networks: node/graph classification, link prediction with GCN, GAT, GraphSAGE, GIN. Message passing, mini-batches, heterogeneous graphs, neighbor sampling, explainability. Supports molecules (QM9, MoleculeNet), social/knowledge graphs, 3D point clouds. For non-graph DL use PyTorch; for classical graph algorithms use NetworkX.
npx skills add BioTender-max/awesome-bio-agent-skills --skill torch-geometric-graph-neural-networks --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PyTorch Geometric (PyG) — Graph Neural Networks ## Overview PyTorch Geometric is a library built on PyTorch for developing and training Graph Neural Networks (GNNs). It provides 40+ convolutional layers, mini-batch processing via block-diagonal adjacency matrices, neighbor sampling for large-scale graphs, and heterogeneous graph support for multi-type node/edge networks. ## When to Use - Node classification on citation, social, or biological networks - Graph-level classification (molecular activity, protein function) - Link prediction (knowledge graphs, recommendation systems) - Molecular property prediction (drug discovery, quantum chemistry) - 3D point cloud processing and mesh analysis - Large-scale graph learning with neighbor sampling (>100K nodes) - Heterogeneous graphs with multiple node/edge types - **For non-graph deep learning** → use PyTorch directly - **For traditional graph algorithms (shortest path, centrality)** → use NetworkX ## Prerequisites ```bash pip install torch torch_geometric # Optional sparse operations (recommended): # pip install pyg_lib torch_scatter torch_sparse torch_cluster ``` ```python import torch import torch.nn.functional as F from torch_geomet
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- 1. Data Representation
- 2. Convolutional Layers
- 3. Custom Message Passing
- 4. Pooling & Graph-Level Readout
- 5. Heterogeneous Graphs
- 6. Transforms & Preprocessing
- Key Concepts
- Layer Selection Guide
- Data Flow Architecture
pip install torch torch_geometric Optional sparse operations (recommended): pip install pyg_lib torch_scatter torch_sparse torch_cluster
What does the torch-geometric-graph-neural-networks skill do?
PyTorch Geometric (PyG) for graph neural networks: node/graph classification, link prediction with GCN, GAT, GraphSAGE, GIN. Message passing, mini-batches, heterogeneous graphs, neighbor sampling, explainability. Supports molecules (QM9, MoleculeNet), social/knowledge graphs, 3D point clouds. For non-graph DL use PyTorch; for classical graph algorithms use NetworkX.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill torch-geometric-graph-neural-networks --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.