torch-geometric
Category: data-science-and-ml · License: MIT · Version: 1.2
Tags: graph neural networks, PyTorch Geometric, GNN, deep learning
When to use it
PyTorch Geometric (PyG) for 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.Example requests
Ask your agent in plain language, for example:- “How do I build a 2-layer GCN for Cora in PyG?”
- “I’m training a GraphSAGE model on ogbn-products with NeighborLoader (num_neighbors=[15,10,5]) for a 2-layer model and my accuracy is bad and training is slow. In the loop I compute loss on out vs batch.y directly. Here’s my loop:
for batch in loader: out = model(batch.x, batch.edge_index); loss = F.cross_entropy(out, batch.y). What’s wrong?” - “I have a CSV of users, a CSV of movies, and a CSV of ratings. I want to predict which movies a user will like, treating users and movies as different kinds of nodes with different relations between them. How should I set this up and train it?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole data-science-and-ml category with /plugin install data-science-and-ml@research-agent-skills.