dsgiitr/graph_nets resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1237 stars · 227 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of PyTorch implementations with accompanying blog-style Jupyter notebooks explaining major graph representation learning papers: DeepWalk, GCN, GraphSAGE, ChebNet, and GAT. It serves as an educational supplement to the DSG IIT Roorkee blog series 'Explained: Graph Representation Learning'.

Use cases

• learn graph neural networks with pytorch
• understand how GCN works with code
• implement DeepWalk node embeddings
• study GraphSAGE inductive learning
• tutorial on graph attention networks
• reproduce graph representation learning papers
• learn ChebNet spectral graph convolution

When to choose

• you want paper implementations paired with intuitive explanations
• you are learning GNNs from scratch with runnable notebooks
• you need reference PyTorch code for classic graph embedding models

When to avoid

• you need a production-ready or maintained GNN library
• you want a broad API covering many GNN architectures
• you require a licensed dependency for commercial use

Facets

learning-resource · maturity maintenance

machine-learning deep-learning machine-learning deep-learning tutorials python graph-neural-networks pytorch node-embeddings jupyter-notebooks educational graph-representation-learning gcn graphsage gat deepwalk algorithms

Member repositories

For agents

markdown · JSON · MCP: product_card(name="dsgiitr/graph_nets")